modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
aaaaaswwe/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret | aaaaaswwe | 2025-06-12T23:33:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am giant pale ferret",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T14:01:16Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am giant pale ferret
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="aaaaaswwe/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-giant_pale_ferret", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
raymunde/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_silky_mandrill | raymunde | 2025-06-12T23:17:12Z | 52 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sedate silky mandrill",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T22:15:11Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_silky_mandrill
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sedate silky mandrill
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_silky_mandrill
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="raymunde/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sedate_silky_mandrill", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
blackbarry33/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil | blackbarry33 | 2025-06-12T22:33:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered grunting gerbil",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T21:06:41Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered grunting gerbil
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="blackbarry33/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_grunting_gerbil", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF | qingy2024 | 2025-06-12T22:28:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"qwen3",
"ui-generation",
"tailwind-css",
"html",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Tesslate/UIGEN-T3-4B-Preview",
"base_model:quantized:Tesslate/UIGEN-T3-4B-Preview",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T22:28:02Z | ---
base_model: Tesslate/UIGEN-T3-4B-Preview
tags:
- text-generation-inference
- transformers
- qwen3
- ui-generation
- tailwind-css
- html
- llama-cpp
- gguf-my-repo
language:
- en
---
# qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF
This model was converted to GGUF format from [`Tesslate/UIGEN-T3-4B-Preview`](https://huggingface.co/Tesslate/UIGEN-T3-4B-Preview) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Tesslate/UIGEN-T3-4B-Preview) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF --hf-file uigen-t3-4b-preview-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF --hf-file uigen-t3-4b-preview-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF --hf-file uigen-t3-4b-preview-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo qingy2024/UIGEN-T3-4B-Preview-Q4_K_M-GGUF --hf-file uigen-t3-4b-preview-q4_k_m.gguf -c 2048
```
|
silento-18k/CLIP.18.silento.video.twitter.silento.video.original | silento-18k | 2025-06-12T20:29:38Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T20:27:29Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=silento)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=silento)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=silento) |
renshanhf/weighted-triplet-finetuned-model | renshanhf | 2025-06-12T20:04:10Z | 0 | 1 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"triplet-loss",
"finance",
"license:mit",
"region:us"
] | null | 2025-06-12T19:41:00Z | ---
license: mit
tags:
- sentence-transformers
- triplet-loss
- finance
---
# Weighted Triplet Fine-Tuned Model
This model is a SentenceTransformer fine-tuned with weighted triplet loss and metadata injection for finance-related semantic search.
## Training Data
- Triplet data (anchor, positive, negative) with metadata (domain, year)
- Example: `[domain:finance] [year:2024] Quarterly report shows revenue growth.`
## Sample Data
You can download the sample dataset used for demonstration here:
[sample_data.json](./sample_data.json)
## Intended Use
- Semantic search and retrieval-augmented generation (RAG) in finance and similar domains.
## Limitations
- Trained on synthetic/small dataset for demonstration.
- Metadata format must match training (e.g., `[domain:finance] [year:2024] ...`).
## Example
```python
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("renshanhf/weighted-triplet-finetuned-model")
embedding = model.encode("[domain:finance] [year:2024] Quarterly report shows revenue growth.")
|
mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF | mradermacher | 2025-06-12T19:23:17Z | 349 | 1 | transformers | [
"transformers",
"gguf",
"image-to-text",
"ar",
"dataset:NAMAA-Space/QariOCR-v0.3-markdown-mixed-dataset",
"base_model:NAMAA-Space/Qari-OCR-v0.3-VL-2B-Instruct",
"base_model:quantized:NAMAA-Space/Qari-OCR-v0.3-VL-2B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | image-to-text | 2025-06-04T08:33:22Z | ---
base_model: NAMAA-Space/Qari-OCR-v0.3-VL-2B-Instruct
datasets:
- NAMAA-Space/QariOCR-v0.3-markdown-mixed-dataset
language:
- ar
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- image-to-text
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NAMAA-Space/Qari-OCR-v0.3-VL-2B-Instruct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged-GGUF/resolve/main/Qari-OCR-0.3-SNAPSHOT-VL-2B-Instruct-merged.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
MetaphoricalCode/Skyfall-36B-v2-exl3-4bpw-hb6 | MetaphoricalCode | 2025-06-12T19:01:47Z | 0 | 0 | null | [
"safetensors",
"mistral",
"base_model:TheDrummer/Skyfall-36B-v2",
"base_model:quantized:TheDrummer/Skyfall-36B-v2",
"license:other",
"4-bit",
"exl3",
"region:us"
] | null | 2025-06-12T18:46:41Z | ---
base_model:
- TheDrummer/Skyfall-36B-v2
base_model_relation: quantized
license: other
---
## Quantized using the default exllamav3 (0.0.3) quantization process.
- Original model: https://huggingface.co/TheDrummer/Skyfall-36B-v2
- exllamav3: https://github.com/turboderp-org/exllamav3
---
# Join our Discord! https://discord.gg/Nbv9pQ88Xb
## Nearly 4000 members strong 💪 Now with more channels! A hub for users and makers alike!
---
[BeaverAI](https://huggingface.co/BeaverAI) proudly presents...
# Skyfall 36B v2
*Skyfall v2 is an upscaled version of Mistral Small 2501 with continued training for creativity and RP.*
## Special Thanks
- Thank you to each and everyone who donated and subscribed in [Ko-Fi](https://ko-fi.com/thedrummer) to make our venture a little bit easier.
- I'm also recently unemployed. I am a Software Developer with 8 years of experience in Web, API, AI, and adapting to new tech and requirements. If you're hiring, feel free to reach out to me however.
- To commercial hosters of my models: If you profit off someone's work, kindly consider contributing to the cause rather than turning a blind eye to those who provide value and are in need. A subscription/donation to my KoFi would be greatly appreciated!
## Supported Chat Templates
- Mistral v7 Tekken (highly recommended)
- Metharme (not recommended)
- Alpaca (may be interesting, especially for cyoa / story)
## Description
> Creativity, good writing style, good instruct, chain of thought capability, mathematics understanding, and solid tool use performance... This model is peak! This will be my new daily model over all the 70Bs I have used.
> Skyfall v2 is without a doubt my favorite model I've ever managed to run locally, bar none
> skyfall is kinda nuts i am quite impressed
> The biggest stand out for me is how good Skyfall handles size differences especially. It actually beats all of the 70b's I have used for descriptions of how the character worked around our size difference.
> I played with the Skyfall 3bit model, taking a new character card with which I had not previously RP'd, and damn, it was so alive! The character's speech was conceptually correct, not as dry as 24b, there was a comedy tag and damn I really laughed in places. I really liked it, maybe it was the specific prompt that played great together with Skyfall.
> Seriously though, Skyfall is just insanely good for some reason
> How did you make skyfall so good
## Links
- Original: https://huggingface.co/TheDrummer/Skyfall-36B-v2
- GGUF: https://huggingface.co/TheDrummer/Skyfall-36B-v2-GGUF
- iMatrix (recommended): https://huggingface.co/bartowski/TheDrummer_Skyfall-36B-v2-GGUF
|
stablediffusionapi/gufengSunxxx | stablediffusionapi | 2025-06-12T18:57:07Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-06-12T18:55:27Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d0e7e4b9-de79-44d9-a560-7eb6d9f29cca/width=450/0043.jpeg
---
# None API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "gufengSunxxx"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/gufengSunxxx)
Model link: [View model](https://modelslab.com/models/gufengSunxxx)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "gufengSunxxx",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN** |
prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora-F32-GGUF | prince4332 | 2025-06-12T18:54:14Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora",
"base_model:quantized:prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T18:54:11Z | ---
base_model: prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora-F32-GGUF
This LoRA adapter was converted to GGUF format from [`prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora`](https://huggingface.co/prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/prince4332/Llama-3.2-3B-ascii-guide-cats-10-ep-lora) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora Llama-3.2-3B-ascii-guide-cats-10-ep-lora-f32.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora Llama-3.2-3B-ascii-guide-cats-10-ep-lora-f32.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
mrsergazinov/q-FrozenLake-v1-4x4-noSlippery | mrsergazinov | 2025-06-12T18:16:14Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-12T18:16:11Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mrsergazinov/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
aieng-lab/starcoder2-7b_commit-intent | aieng-lab | 2025-06-12T18:10:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-classification",
"en",
"base_model:bigcode/starcoder2-7b",
"base_model:finetune:bigcode/starcoder2-7b",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T18:06:07Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bigcode/starcoder2-7b
pipeline_tag: text-classification
---
# StarCoder2 7b for classifying commit messages
This model classifies commit messages in code repositories (e.g., GitHub) as 'perfective', 'corrective' or 'other'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bigcode/starcoder2-7b](https://huggingface.co/bigcode/starcoder2-7b)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
BootesVoid/cmbtmk4sn00dljhfok2ihyxke_cmbtmso0q00evjhfo4kpgszbv | BootesVoid | 2025-06-12T18:05:50Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T18:05:48Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ALISON
---
# Cmbtmk4Sn00Dljhfok2Ihyxke_Cmbtmso0Q00Evjhfo4Kpgszbv
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ALISON` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ALISON",
"lora_weights": "https://huggingface.co/BootesVoid/cmbtmk4sn00dljhfok2ihyxke_cmbtmso0q00evjhfo4kpgszbv/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbtmk4sn00dljhfok2ihyxke_cmbtmso0q00evjhfo4kpgszbv', weight_name='lora.safetensors')
image = pipeline('ALISON').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbtmk4sn00dljhfok2ihyxke_cmbtmso0q00evjhfo4kpgszbv/discussions) to add images that show off what you’ve made with this LoRA.
|
aieng-lab/starcoder2-3b_commit-intent | aieng-lab | 2025-06-12T18:00:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-classification",
"en",
"base_model:bigcode/starcoder2-3b",
"base_model:finetune:bigcode/starcoder2-3b",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T17:58:35Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bigcode/starcoder2-3b
pipeline_tag: text-classification
---
# StarCoder2 3b for classifying commit messages
This model classifies commit messages in code repositories (e.g., GitHub) as 'perfective', 'corrective' or 'other'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
jabomi8/Thomas | jabomi8 | 2025-06-12T17:55:06Z | 0 | 1 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T17:26:44Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Thomas
---
# Thomas
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Thomas ` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Thomas ",
"lora_weights": "https://huggingface.co/jabomi8/Thomas/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jabomi8/Thomas', weight_name='lora.safetensors')
image = pipeline('Thomas ').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jabomi8/Thomas/discussions) to add images that show off what you’ve made with this LoRA.
|
HintonZhang50/medgemma-4b-pt-sft-qlora-fac3k | HintonZhang50 | 2025-06-12T17:45:33Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-10T13:39:29Z | ---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-pt-sft-qlora-fac3k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-pt-sft-qlora-fac3k
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="HintonZhang50/medgemma-4b-pt-sft-qlora-fac3k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fast_fishy_opossum | haedahae | 2025-06-12T17:30:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fast fishy opossum",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T03:26:49Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fast_fishy_opossum
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fast fishy opossum
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fast_fishy_opossum
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fast_fishy_opossum", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
18-SMS-Rani-Viral-Video-Original-Link-X/FULL.VIDEO.Sms.Rani.Viral.Video.Tutorial.Official | 18-SMS-Rani-Viral-Video-Original-Link-X | 2025-06-12T17:26:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T17:25:57Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
gradientrouting-spar/gcd_syco_capitalspositive_neg_prx_neg_prx-None_lambda_proxy-1.0_seed_42 | gradientrouting-spar | 2025-06-12T17:24:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T00:04:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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qubvel-hf/vjepa2-vitl-fpc32-256-diving48 | qubvel-hf | 2025-06-12T17:24:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vjepa2",
"video",
"video-classification",
"dataset:bkprocovid19/diving48",
"license:mit",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-06-12T17:14:17Z | ---
license: mit
pipeline_tag: video-classification
tags:
- video
library_name: transformers
datasets:
- bkprocovid19/diving48
---
# V-JEPA 2
A frontier video understanding model developed by FAIR, Meta, which extends the pretraining objectives of [VJEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/), resulting in state-of-the-art video understanding capabilities, leveraging data and model sizes at scale.
The code is released [in this repository](https://github.com/facebookresearch/vjepa2).
<div style="background-color: rgba(251, 255, 120, 0.4); padding: 10px; color: black; border-radius: 5px; box-shadow: 0 4px 8px rgba(0,0,0,0.1);">
💡 This is V-JEPA 2 model with video classification head pretrained on <a href="http://www.svcl.ucsd.edu/projects/resound/dataset.html" style="color: black;">Diving 48</a> dataset.
</div>
<br></br>
<img src="https://dl.fbaipublicfiles.com/vjepa2/vjepa2-pretrain.gif">
## Installation
To run V-JEPA 2 model, ensure you have installed the latest transformers:
```bash
pip install -U git+https://github.com/huggingface/transformers
```
## Video classification code snippet
```python
import torch
import numpy as np
from torchcodec.decoders import VideoDecoder
from transformers import AutoVideoProcessor, AutoModelForVideoClassification
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model and video preprocessor
hf_repo = "facebook/vjepa2-vitl-fpc32-256-diving48"
model = AutoModelForVideoClassification.from_pretrained(hf_repo).to(device)
processor = AutoVideoProcessor.from_pretrained(hf_repo)
# To load a video, sample the number of frames according to the model.
video_url = "https://huggingface.co/qubvel-hf/vjepa2-vitl-fpc32-256-diving48/resolve/main/sample/diving.mp4"
vr = VideoDecoder(video_url)
frame_idx = np.arange(0, model.config.frames_per_clip, 8) # you can define more complex sampling strategy
video = vr.get_frames_at(indices=frame_idx).data # frames x channels x height x width
# Preprocess and run inference
inputs = processor(video, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
print("Top 5 predicted class names:")
top5_indices = logits.topk(5).indices[0]
top5_probs = torch.softmax(logits, dim=-1).topk(5).values[0]
for idx, prob in zip(top5_indices, top5_probs):
text_label = model.config.id2label[idx.item()]
print(f" - {text_label}: {prob:.2f}")
```
Output:
```
Top 5 predicted class names:
- ['Reverse', 'Dive', 'NoTwis', 'PIKE']: 0.52
- ['Inward', '25som', 'NoTwis', 'PIKE']: 0.12
- ['Forward', '35som', 'NoTwis', 'PIKE']: 0.07
- ['Reverse', '25som', 'NoTwis', 'PIKE']: 0.05
- ['Forward', '25som', '1Twis', 'PIKE']: 0.03
```
## Citation
```
@techreport{assran2025vjepa2,
title={V-JEPA~2: Self-Supervised Video Models Enable Understanding, Prediction and Planning},
author={Assran, Mahmoud and Bardes, Adrien and Fan, David and Garrido, Quentin and Howes, Russell and
Komeili, Mojtaba and Muckley, Matthew and Rizvi, Ammar and Roberts, Claire and Sinha, Koustuv and Zholus, Artem and
Arnaud, Sergio and Gejji, Abha and Martin, Ada and Robert Hogan, Francois and Dugas, Daniel and
Bojanowski, Piotr and Khalidov, Vasil and Labatut, Patrick and Massa, Francisco and Szafraniec, Marc and
Krishnakumar, Kapil and Li, Yong and Ma, Xiaodong and Chandar, Sarath and Meier, Franziska and LeCun, Yann and
Rabbat, Michael and Ballas, Nicolas},
institution={FAIR at Meta},
year={2025}
}
``` |
aieng-lab/ModernBERT-large_commit-intent | aieng-lab | 2025-06-12T17:24:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"en",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T17:23:44Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- answerdotai/ModernBERT-large
pipeline_tag: text-classification
---
# ModernBERT large for classifying commit messages
This model classifies commit messages in code repositories (e.g., GitHub) as 'perfective', 'corrective' or 'other'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
aieng-lab/roberta-base_commit-intent | aieng-lab | 2025-06-12T17:21:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"en",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T17:21:34Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- roberta-base
pipeline_tag: text-classification
---
# RoBERTa base for classifying commit messages
This model classifies commit messages in code repositories (e.g., GitHub) as 'perfective', 'corrective' or 'other'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [roberta-base](https://huggingface.co/roberta-base)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
mohannad-tazi/Sys_eng_lora_model | mohannad-tazi | 2025-06-12T17:16:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T17:16:00Z | ---
base_model: unsloth/mistral-7b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mohannad-tazi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
morturr/Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-18-2025-06-12 | morturr | 2025-06-12T17:15:16Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T17:15:04Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-18-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-18-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
gradientrouting-spar/gcd_syco_capitalspositive_neg_prx_neg_prx-None_lambda_proxy-0.5_seed_5 | gradientrouting-spar | 2025-06-12T16:58:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T23:44:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PORTULAN/gervasio-7b-portuguese-ptpt-decoder | PORTULAN | 2025-06-12T16:44:56Z | 4,361 | 8 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"gervasio-pt*",
"gervasio-ptpt",
"gervasio-ptbr",
"gervasio-7b-portuguese-ptpt-decoder",
"gervasio-7b-portuguese-ptbr-decoder",
"portulan",
"albertina-pt*",
"clm",
"gpt",
"portuguese",
"decoder",
"foundation model",
"pt",
"dataset:PORTULAN/extraglue",
"dataset:PORTULAN/extraglue-instruct",
"arxiv:2402.18766",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-11-15T14:49:31Z | ---
license: mit
language:
- pt
tags:
- gervasio-pt*
- gervasio-ptpt
- gervasio-ptbr
- gervasio-7b-portuguese-ptpt-decoder
- gervasio-7b-portuguese-ptbr-decoder
- portulan
- albertina-pt*
- clm
- gpt
- portuguese
- decoder
- foundation model
datasets:
- PORTULAN/extraglue
- PORTULAN/extraglue-instruct
---
</br>
</br>
<img align="left" width="40" height="40" src="https://github.githubassets.com/images/icons/emoji/unicode/1f917.png">
<p style="text-align: center;"> This is the model card for Gervásio 7B PTPT Decoder.
You may be interested in some of the other models in the <a href="https://huggingface.co/PORTULAN">Albertina (encoders), Gervásio (decoders) and Serafim (sentence encoder) families</a>.
</p>
</br>
</br>
# Gervásio 7B PTPT
</br>
This model has been **deprecated**.
We recommend you use the improved [**gervasio-8b-portuguese-ptpt-decoder**](https://huggingface.co/PORTULAN/gervasio-8b-portuguese-ptpt-decoder).
<!--
**Gervásio PT*** is a **fully open** decoder for the **Portuguese language**.
It is a **decoder** of the LLaMA family, based on the neural architecture Transformer and developed over the LLaMA-2 7B model.
Its further improvement through additional training was done over language resources that include new instruction data sets of Portuguese prepared for this purpose ([extraGLUE-Instruct
](https://huggingface.co/datasets/PORTULAN/extraglue-instruct)).
It has different versions that were trained for different variants of Portuguese (PT),
namely for the European variant, spoken in Portugal ([**gervasio-7b-portuguese-ptpt-decoder**](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptpt-decoder)), and for the American variant, spoken in Brazil ([**gervasio-7b-portuguese-ptbr-decoder**](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptbr-decoder)).
All versions of Gervásio are **openly distributed for free under an open license**, including thus for research and commercial purposes, and given its size, can
be run on consumer-grade hardware.
**Gervásio 7B PTPT** is developed by NLX-Natural Language and Speech Group, at the University of Lisbon, Faculty of Sciences, Department of Informatics, Portugal.
For the record, its full name is **Gervásio Produz Textos em Português**, to which corresponds the natural acronym **GPT PT**,
and which is known more shortly as **Gervásio PT*** or, even more briefly, just as **Gervásio**, among its acquaintances.
Gervásio 7B PTPT is developed by a team from the University of Lisbon, Portugal.
For a fully detailed description, check the respective [publication](https://arxiv.org/abs/2402.18766):
``` latex
@misc{gervasio,
title={Advancing Generative AI for Portuguese with
Open Decoder Gervásio PT-*},
author={Rodrigo Santos, João Silva, Luís Gomes,
João Rodrigues, António Branco},
year={2024},
eprint={2402.18766},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
Please use the above cannonical reference when using or citing this model.
<br>
# Model Description
**This model card is for Gervásio 7B PTPT**, with 7 billion parameters, a hidden size of 4,096 units, an intermediate size of 11,008 units, 32 attention heads, 32 hidden layers, and a tokenizer obtained using the Byte-Pair Encoding (BPE) algorithm implemented with SentencePiece, featuring a vocabulary size of 32,000.
Gervásio 7B PTPT is distributed under an [MIT license](https://huggingface.co/PORTULAN/gervasio-7b-portuguese-ptpt-decoder/blob/main/LICENSE).
<br>
# Training Data
**Gervásio 7B PTPT** was trained over standard supervised fine-tuning, and to keep some alignment with mainstream benchmarks for English, we resorted to tasks and respective datasets in the GLUE and the SuperGLUE collections.
We selected those datasets where the outcome of their machine translation into European Portuguese could preserve, in the target language, the linguistic properties at stake.
From GLUE, we resorted to the following four tasks:
- MRPC (paraphrase Detection).
- RTE (recognizing Textual Entailment).
- STS-B (semantic textual similarity).
- WNLI (coreference and natural language inference).
And from SuperGLUE, we included these other four tasks:
- BoolQ (yes/no question answering).
- CB (inference with 3 labels).
- COPA (reasoning)
- MultiRC (question answering).
These datasets were machine translated into European Portuguese and from the [extraGLUE](https://huggingface.co/datasets/PORTULAN/extraglue) dataset.
Furthermore, instruction templates have been manually crafted for each task.
These take the various fields in the dataset and arrange them into prompts, which were collected into the [extraGLUE-instruct](https://huggingface.co/datasets/PORTULAN/extraglue-instruct) dataset.
We also employed data augmentation techniques to enhance the size and diversity of our dataset.
This involved repurposing the tasks in various ways, such as generation of answers from MultiRC, question generation from BoolQ, and other relevant modifications.
# Training Details
We applied supervised fine-tuning with a causal language modeling training objective following a zero-out technique during the fine-tuning process.
Specifically, while the entire prompt received attention during fine-tuning, only the response tokens were subjected to back-propagation.
In terms of hyper-parameters, the model was trained with a learning rate of 2 * 10^-5, a weight decay of 0.1, a two-epoch training regime without warm-up, and to ensure the same number of tokens back-propagated per step, we employed an input sequence of 512 tokens with a batch size of 16 and 16 accumulation steps.
Due to hardware limitations that imposed a shorter sequence length (512) compared to the base model (4096), instead of the typical practice of concatenating all training examples and then dividing them into batches with the same input sequence length, we separated each example individually.
In other words, each example occupies the full input sequence length.
# Performance
For testing, we reserved the translated datasets MRPC (similarity) and RTE (inference), from GLUE, and COPA (reasoning/qa), from SuperGLUE, which were taking as representatives of three major types of tasks, and were not seen during training.
| Model | MRPC (F1) | RTE (F1) | COPA (F1) |
|--------------------------|----------------|----------------|-----------|
| **Gervásio 7B PTPT** | **0.7273** | **0.8291** | **0.5459**|
| **LLaMA-2 (English)** | 0.0328 | 0.0482 | 0.3844 |
| **LLaMA-2 Chat (English)** | 0.5703 | 0.4697 | 0.4737 |
<br>
# How to use
You can use this model directly with a pipeline for causal language modeling:
```python3
>>> from transformers import pipeline
>>> generator = pipeline(model='PORTULAN/gervasio-7b-portuguese-ptpt-decoder')
>>> generator("A comida portuguesa é", max_new_tokens=10)
```
<br>
# Acknowledgments
The research reported here was partially supported by: PORTULAN CLARIN—Research Infrastructure for the Science and Technology of Language,
funded by Lisboa 2020, Alentejo 2020 and FCT—Fundação para a Ciência e Tecnologia under the
grant PINFRA/22117/2016; research project GPT-PT - Transformer-based Decoder for the Portuguese Language, funded by FCT—Fundação para a Ciência e Tecnologia under the
grant CPCA-IAC/AV/478395/2022; innovation project
ACCELERAT.AI - Multilingual Intelligent Contact Centers, funded by IAPMEI, I.P. - Agência para a Competitividade e Inovação
under the grant C625734525-00462629, of Plano de Recuperação e Resiliência,
call RE-C05-i01.01 – Agendas/Alianças Mobilizadoras para a Reindustrialização.
-->
|
YuchenLi01/genv3pair1NoGT_1.5B_sft_prompt_completion_ebs32_lr1e-06_epoch1.0_42 | YuchenLi01 | 2025-06-12T16:41:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T16:23:56Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1
model-index:
- name: genv3pair1NoGT_1.5B_sft_prompt_completion_ebs32_lr1e-06_epoch1.0_42
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# genv3pair1NoGT_1.5B_sft_prompt_completion_ebs32_lr1e-06_epoch1.0_42
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv3pair1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0636
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4267 | 0.1117 | 20 | 0.4130 |
| 0.1711 | 0.2235 | 40 | 0.1768 |
| 0.1148 | 0.3352 | 60 | 0.1007 |
| 0.0624 | 0.4469 | 80 | 0.0680 |
| 0.0711 | 0.5587 | 100 | 0.0654 |
| 0.0642 | 0.6704 | 120 | 0.0644 |
| 0.0696 | 0.7821 | 140 | 0.0638 |
| 0.0672 | 0.8939 | 160 | 0.0635 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.20.3
|
gradientrouting-spar/gcd_syco_capitalsst_we_limit_proxy_data_to-1_pos_prx-proxy_neg_prx-proxy_neg_st_alpha-0.8_seed_1 | gradientrouting-spar | 2025-06-12T16:36:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T16:36:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BootesVoid/cmb9r7xbi0hiq1b1y6f628wrj_cmbtkoy03008ojhfo1592uakm | BootesVoid | 2025-06-12T16:34:36Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T16:34:34Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LIATWO
---
# Cmb9R7Xbi0Hiq1B1Y6F628Wrj_Cmbtkoy03008Ojhfo1592Uakm
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LIATWO` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LIATWO",
"lora_weights": "https://huggingface.co/BootesVoid/cmb9r7xbi0hiq1b1y6f628wrj_cmbtkoy03008ojhfo1592uakm/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb9r7xbi0hiq1b1y6f628wrj_cmbtkoy03008ojhfo1592uakm', weight_name='lora.safetensors')
image = pipeline('LIATWO').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb9r7xbi0hiq1b1y6f628wrj_cmbtkoy03008ojhfo1592uakm/discussions) to add images that show off what you’ve made with this LoRA.
|
gradientrouting-spar/gcd_syco_modkl_div_beta_kl-100_seed_1 | gradientrouting-spar | 2025-06-12T16:19:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T16:19:16Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
johngreendr1/807cd0ea-7e61-4118-98b4-eb37bd129ba4 | johngreendr1 | 2025-06-12T16:13:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-3B-Instruct",
"region:us"
] | null | 2025-06-12T14:24:20Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1 |
taybihassan0/test | taybihassan0 | 2025-06-12T16:13:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T16:13:48Z | ---
license: apache-2.0
---
|
morturr/Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-7-2025-06-12 | morturr | 2025-06-12T16:08:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T16:08:35Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-7-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_amazon_headlines-COMB-amazon-comb-2-seed-7-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
MahiH/dialogpt-finetuned-chatbot | MahiH | 2025-06-12T16:06:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T16:02:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BootesVoid/cmbialuak096hkfxsrsdzdqy9_cmbtiuipu002yjhfoqdxcjl56 | BootesVoid | 2025-06-12T15:40:16Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-12T15:40:13Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: KELSIE
---
# Cmbialuak096Hkfxsrsdzdqy9_Cmbtiuipu002Yjhfoqdxcjl56
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `KELSIE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "KELSIE",
"lora_weights": "https://huggingface.co/BootesVoid/cmbialuak096hkfxsrsdzdqy9_cmbtiuipu002yjhfoqdxcjl56/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbialuak096hkfxsrsdzdqy9_cmbtiuipu002yjhfoqdxcjl56', weight_name='lora.safetensors')
image = pipeline('KELSIE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbialuak096hkfxsrsdzdqy9_cmbtiuipu002yjhfoqdxcjl56/discussions) to add images that show off what you’ve made with this LoRA.
|
hungnguyen2k4/rtdetr-r50-fruits-finetune | hungnguyen2k4 | 2025-06-12T15:27:13Z | 83 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"rt_detr",
"object-detection",
"generated_from_trainer",
"base_model:PekingU/rtdetr_r50vd_coco_o365",
"base_model:finetune:PekingU/rtdetr_r50vd_coco_o365",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-06-10T04:09:14Z | ---
library_name: transformers
license: apache-2.0
base_model: PekingU/rtdetr_r50vd_coco_o365
tags:
- generated_from_trainer
model-index:
- name: rtdetr-r50-fruits-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rtdetr-r50-fruits-finetune
This model is a fine-tuned version of [PekingU/rtdetr_r50vd_coco_o365](https://huggingface.co/PekingU/rtdetr_r50vd_coco_o365) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 9.1504
- Map: 0.5914
- Map 50: 0.7257
- Map 75: 0.619
- Map Small: 0.2732
- Map Medium: 0.5577
- Map Large: 0.7578
- Mar 1: 0.2874
- Mar 10: 0.6275
- Mar 100: 0.7381
- Mar Small: 0.4284
- Mar Medium: 0.7129
- Mar Large: 0.8936
- Map Apple: 0.5473
- Mar 100 Apple: 0.7271
- Map Banana: 0.5994
- Mar 100 Banana: 0.7667
- Map Grapes: 0.488
- Mar 100 Grapes: 0.6322
- Map Orange: 0.5429
- Mar 100 Orange: 0.6694
- Map Pineapple: 0.6446
- Mar 100 Pineapple: 0.7761
- Map Watermelon: 0.726
- Mar 100 Watermelon: 0.8571
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Apple | Mar 100 Apple | Map Banana | Mar 100 Banana | Map Grapes | Mar 100 Grapes | Map Orange | Mar 100 Orange | Map Pineapple | Mar 100 Pineapple | Map Watermelon | Mar 100 Watermelon |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:---------:|:-------------:|:----------:|:--------------:|:----------:|:--------------:|:----------:|:--------------:|:-------------:|:-----------------:|:--------------:|:------------------:|
| 41.5818 | 1.0 | 750 | 12.6083 | 0.4617 | 0.5891 | 0.4851 | 0.1685 | 0.3966 | 0.6416 | 0.2591 | 0.5875 | 0.7022 | 0.3924 | 0.6828 | 0.8616 | 0.4446 | 0.6799 | 0.4937 | 0.7286 | 0.3495 | 0.5824 | 0.4354 | 0.6361 | 0.5126 | 0.7504 | 0.5342 | 0.8361 |
| 15.5004 | 2.0 | 1500 | 10.8701 | 0.5095 | 0.6394 | 0.5327 | 0.1842 | 0.4477 | 0.6915 | 0.2708 | 0.6038 | 0.7186 | 0.3804 | 0.7018 | 0.8764 | 0.4785 | 0.7068 | 0.5179 | 0.7374 | 0.4246 | 0.6226 | 0.496 | 0.6515 | 0.5696 | 0.7558 | 0.5703 | 0.8378 |
| 14.3891 | 3.0 | 2250 | 10.4221 | 0.5418 | 0.6744 | 0.5724 | 0.206 | 0.5152 | 0.7053 | 0.2761 | 0.6196 | 0.7281 | 0.3968 | 0.709 | 0.8809 | 0.5209 | 0.7177 | 0.5413 | 0.7476 | 0.4373 | 0.6269 | 0.5007 | 0.6551 | 0.5928 | 0.7757 | 0.6577 | 0.8455 |
| 13.2571 | 4.0 | 3000 | 10.0221 | 0.5545 | 0.6826 | 0.5835 | 0.2138 | 0.5192 | 0.7231 | 0.279 | 0.621 | 0.7326 | 0.4118 | 0.7143 | 0.8847 | 0.4843 | 0.7082 | 0.5623 | 0.7527 | 0.4584 | 0.6253 | 0.5159 | 0.661 | 0.6081 | 0.7909 | 0.6978 | 0.8574 |
| 12.6793 | 5.0 | 3750 | 9.6700 | 0.5676 | 0.6988 | 0.5956 | 0.23 | 0.527 | 0.7376 | 0.2816 | 0.6221 | 0.7345 | 0.4189 | 0.7134 | 0.8887 | 0.5259 | 0.7169 | 0.568 | 0.7527 | 0.4774 | 0.647 | 0.5262 | 0.6631 | 0.6207 | 0.7772 | 0.6872 | 0.8503 |
| 11.9269 | 6.0 | 4500 | 9.8151 | 0.5609 | 0.6934 | 0.589 | 0.2515 | 0.5278 | 0.726 | 0.2811 | 0.6225 | 0.7308 | 0.4278 | 0.7029 | 0.8868 | 0.5185 | 0.7192 | 0.5623 | 0.7524 | 0.4725 | 0.636 | 0.5158 | 0.6568 | 0.6137 | 0.7786 | 0.6822 | 0.8418 |
| 11.5212 | 7.0 | 5250 | 9.4377 | 0.5727 | 0.7008 | 0.5986 | 0.2439 | 0.5372 | 0.7428 | 0.2817 | 0.6212 | 0.7337 | 0.401 | 0.7099 | 0.8904 | 0.5276 | 0.7209 | 0.59 | 0.7643 | 0.488 | 0.6357 | 0.526 | 0.6631 | 0.6007 | 0.7703 | 0.704 | 0.8477 |
| 11.1832 | 8.0 | 6000 | 9.2311 | 0.59 | 0.7249 | 0.6189 | 0.2746 | 0.5608 | 0.7538 | 0.2857 | 0.6313 | 0.7424 | 0.436 | 0.7166 | 0.8962 | 0.5511 | 0.7284 | 0.5896 | 0.7602 | 0.4869 | 0.642 | 0.5419 | 0.6667 | 0.6545 | 0.8 | 0.7162 | 0.8574 |
| 10.9981 | 9.0 | 6750 | 9.2291 | 0.5892 | 0.7238 | 0.6149 | 0.2703 | 0.549 | 0.759 | 0.2859 | 0.628 | 0.7388 | 0.4296 | 0.7094 | 0.8955 | 0.5504 | 0.7282 | 0.5888 | 0.7664 | 0.4849 | 0.6312 | 0.5374 | 0.6698 | 0.6471 | 0.7786 | 0.7266 | 0.8585 |
| 10.7438 | 10.0 | 7500 | 9.1504 | 0.5914 | 0.7257 | 0.619 | 0.2732 | 0.5577 | 0.7578 | 0.2874 | 0.6275 | 0.7381 | 0.4284 | 0.7129 | 0.8936 | 0.5473 | 0.7271 | 0.5994 | 0.7667 | 0.488 | 0.6322 | 0.5429 | 0.6694 | 0.6446 | 0.7761 | 0.726 | 0.8571 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
HusnainM7/psychology-model_key | HusnainM7 | 2025-06-12T15:22:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"region:us"
] | null | 2025-06-12T15:22:40Z | ---
base_model: microsoft/phi-2
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.05_0.25_0.05_epoch1 | MinaMila | 2025-06-12T15:10:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T15:08:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF | yaggy | 2025-06-12T14:48:38Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"general-purpose",
"roleplay",
"storywriting",
"chemistry",
"biology",
"code",
"climate",
"instruct",
"chatml",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:h34v7/Euro-DDXPv1.0",
"base_model:quantized:h34v7/Euro-DDXPv1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-12T14:47:29Z | ---
base_model: h34v7/Euro-DDXPv1.0
library_name: transformers
tags:
- mergekit
- merge
- general-purpose
- roleplay
- storywriting
- chemistry
- biology
- code
- climate
- instruct
- chatml
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF
This model was converted to GGUF format from [`h34v7/Euro-DDXPv1.0`](https://huggingface.co/h34v7/Euro-DDXPv1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/h34v7/Euro-DDXPv1.0) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF --hf-file euro-ddxpv1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF --hf-file euro-ddxpv1.0-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF --hf-file euro-ddxpv1.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo yaggy/Euro-DDXPv1.0-Q4_K_S-GGUF --hf-file euro-ddxpv1.0-q4_k_s.gguf -c 2048
```
|
Rusik11/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sizable_hunting_cockroach | Rusik11 | 2025-06-12T14:44:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sizable hunting cockroach",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T09:15:26Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sizable_hunting_cockroach
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sizable hunting cockroach
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sizable_hunting_cockroach
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Rusik11/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sizable_hunting_cockroach", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
FormlessAI/237f4843-baad-4caf-b8ac-e32fe3d55694 | FormlessAI | 2025-06-12T14:42:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"unsloth",
"arxiv:2402.03300",
"base_model:unsloth/llama-3-8b",
"base_model:finetune:unsloth/llama-3-8b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T11:17:36Z | ---
base_model: unsloth/llama-3-8b
library_name: transformers
model_name: 237f4843-baad-4caf-b8ac-e32fe3d55694
tags:
- generated_from_trainer
- trl
- grpo
- unsloth
licence: license
---
# Model Card for 237f4843-baad-4caf-b8ac-e32fe3d55694
This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/237f4843-baad-4caf-b8ac-e32fe3d55694", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/cbogruk4)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
schaeff/gpt2-medium_LNFree500 | schaeff | 2025-06-12T14:40:11Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"base_model:openai-community/gpt2-medium",
"base_model:finetune:openai-community/gpt2-medium",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-06T16:50:11Z | ---
library_name: transformers
license: other
license_name: "openai-gpt2-license"
license_link: "https://github.com/openai/gpt-2/blob/master/LICENSE"
base_model:
- openai-community/gpt2-medium
tags: []
---
# Model Card for `schaeff/gpt2-medium_LNFree500`
Associated publication: *Transformers Don’t Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretability* (arXiv TBD)
Associated GitHub: [removing-layer-norm](https://github.com/submarat/removing-layer-norm)
This model is based on *openai-community/gpt2-medium* and was finetuned on OpenWebText for 500 iterations with 0.5M tokens per iteration. During the finetuning, LayerNorm modules were sequentially disabled. More details on the disabling procedure can be found in the associated publication.
## Usage
This model uses the standard `GPT2LMHeadModel` architecture to avoid requiring `trust_remote_code=True`. While LayerNorm blocks are technically present, they have been effectively disabled through parameter manipulation.
**How LayerNorm is disabled:**
- **Epsilon values**: Set to 1e12 (extremely large), so LayerNorm has no normalizing effect
- **Scale parameters**: Set to 1e6 to counteract the large epsilon value
This approach maintains compatibility with the standard GPT-2 architecture while effectively creating a LayerNorm-free model.
**Complete LayerNorm removal:**
If you want to fully remove LayerNorm operations, you can replace `ln_1`, `ln_2` and `ln_f` modules with identity functions.
Loading instructions:
You can load the model with `transformers`:
```python
model = GPT2LMHeadModel.from_pretrained("schaeff/gpt2-medium_LNFree500")
```
The LayerNorm module inside transformers will not affect the model due to the parameter manipulation. Howevr, this is a bit hacky and we recommend properly the replacing LayerNorm modules with the identity in either TransformerLens or NNSight.
### TransformerLens and NNSight loading code
```python
import torch
from transformers import GPT2LMHeadModel
from transformer_lens import HookedTransformer
model = GPT2LMHeadModel.from_pretrained("schaeff/gpt2-medium_LNFree500").to("cpu")
# Undo hacky LayerNorm removal
for block in model.transformer.h:
block.ln_1.weight.data = block.ln_1.weight.data / 1e6
block.ln_1.eps = 1e-5
block.ln_2.weight.data = block.ln_2.weight.data / 1e6
block.ln_2.eps = 1e-5
model.transformer.ln_f.weight.data = model.transformer.ln_f.weight.data / 1e6
model.transformer.ln_f.eps = 1e-5
# Properly replace LayerNorms by Identities
def removeLN(transformer_lens_model):
for i in range(len(transformer_lens_model.blocks)):
transformer_lens_model.blocks[i].ln1 = torch.nn.Identity()
transformer_lens_model.blocks[i].ln2 = torch.nn.Identity()
transformer_lens_model.ln_final = torch.nn.Identity()
# transformer_lens
hooked_model = HookedTransformer.from_pretrained("gpt2", hf_model=model, fold_ln=True, center_unembed=False).to("cpu")
removeLN(hooked_model)
# NNSight:
from nnsight.models.UnifiedTransformer import UnifiedTransformer
model_nnsight = UnifiedTransformer(model="gpt2", hf_model=model, fold_ln=True, center_unembed=False).to("cpu")
removeLN(model_nnsight)
```
This example code is based on [Logan Riggs' comment](https://www.lesswrong.com/posts/THzcKKQd4oWkg4dSP/you-can-remove-gpt2-s-layernorm-by-fine-tuning-for-an-hour?commentId=Gcq8wic9WmdnqM2Fm).
We recommend to look at [removing-layer-norm](https://github.com/submarat/removing-layer-norm) for seeing the entire workflow of removal, upload, and loading LN free models. In particular, the function `remove_layernorm` in `utils.py` for details on the parameter hack and `eval.py` for loading.
## Model Collection
This model is part of a collection of LayerNorm-free models. The table below provides links and details.
### Evaluation results of LN-free, vanilla fine-tuned, and original GPT-2 models
*Reported values are mean cross-entropy losses for 10.2M tokens for The Pile and The Pile filtered and 4.5M tokens for the OpenWebText (WT) validation set. For each model size and dataset, the lowest loss is highlighted in **bold**, and the loss difference between the LN-free model and the best-performing model is shown in brackets.*
| Model | FT steps | [OWT (val)](https://huggingface.co/datasets/Skylion007/openwebtext) | [The Pile](https://huggingface.co/datasets/apollo-research/monology-pile-uncopyrighted-tokenizer-gpt2) | [The Pile-filtered](https://huggingface.co/datasets/lucabaroni/apollo-pile-filtered-10k) |
|-------|----------|-----------|----------|-------------------|
| OpenAI [GPT-2 Small original](https://huggingface.co/openai-community/gpt2) | 0 | 3.1006 | **2.8450** | **2.7899** |
| schaeff [GPT-2 Small vanilla](https://huggingface.co/schaeff/gpt2-small_vanilla300) | 300 | **3.0126** | 2.8511 | 2.8112 |
| schaeff [GPT-2 Small LN-free](https://huggingface.co/schaeff/gpt2-small_LNFree300) | 300 | 3.0797 [+0.0671] | 2.8852 [+0.0402] | 2.8757 [+0.0858] |
||||||
| OpenAI [GPT-2 Medium original](https://huggingface.co/openai-community/gpt2-medium) | 0 | 2.8145 | **2.5163** | **2.5390** |
| schaeff [GPT-2 Medium vanilla](https://huggingface.co/schaeff/gpt2-medium_vanilla500) | 500 | **2.7390** | 2.5752 | 2.5724 |
| schaeff [GPT-2 Medium LN-free](https://huggingface.co/schaeff/gpt2-medium_LNFree500) | 500 | 2.7642 [+0.0252] | 2.6579 [+0.1416] | 2.6352 [+0.0962] |
||||||
| OpenAI [GPT-2 Large original](https://huggingface.co/openai-community/gpt2-large) | 0 | 2.6623 | **2.5320** | **2.4347** |
| schaeff [GPT-2 Large vanilla](https://huggingface.co/schaeff/gpt2-large_vanilla600) | 600 | **2.6240** | 2.6233 | 2.5074 |
| schaeff [GPT-2 Large LN-free](https://huggingface.co/schaeff/gpt2-large_LNFree600) | 600 | 2.6384 [+0.0144] | 2.7504 [+0.2184] | 2.5159 [+0.0812] |
||||||
| OpenAI [GPT-2 XL original](https://huggingface.co/openai-community/gpt2-xl) | 0 | 2.5567 | **2.4436**¹ | **2.3739** |
| schaeff [GPT-2 XL vanilla](https://huggingface.co/schaeff/gpt2-xl_vanilla800) | 800 | **2.4799** | 2.4673 | 2.3821 |
| schaeff [GPT-2 XL LN-free](https://huggingface.co/schaeff/gpt2-xl_LNFree800) | 800 | 2.5052 [+0.0253] | 130.2197² | 2.3992 [+0.0253] |
#### **Footnotes:**
1. GPT-2 XL original: Median: 1.0103, 95 Percentile Range: [0.0005, 10.6193], 99.9% Percentile Range [≈0.0000, 43.0064]
2. GPT-2 XL LN-free: Median: 1.0937, 95 Percentile Range: [0.0004, 10.7548], 99.9% Percentile Range [≈0.0000, 48.6459]
## Citation
Title: *Transformers Don’t Need LayerNorm at Inference Time: Scaling LayerNorm Removal to GPT-2 XL and the Implications for Mechanistic Interpretability*
**BibTeX:**
[TBD]
|
hafidhsoekma/gasing-edu-16bit | hafidhsoekma | 2025-06-12T14:31:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T14:25:58Z | ---
base_model: unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hafidhsoekma
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
geetu040/deepseek-vl-7b-chat | geetu040 | 2025-06-12T14:27:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deepseek_vl_hybrid",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-12T08:54:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
erdem-erdem/llama3.2-3b-it-qwq-sft-tpt-iter1-r64-ps-grpo-r32 | erdem-erdem | 2025-06-12T14:26:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:erdem-erdem/llama3.2-3b-it-qwq-sft-tpt-iter1-r64",
"base_model:finetune:erdem-erdem/llama3.2-3b-it-qwq-sft-tpt-iter1-r64",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T14:24:58Z | ---
base_model: erdem-erdem/llama3.2-3b-it-qwq-sft-tpt-iter1-r64
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** erdem-erdem/llama3.2-3b-it-qwq-sft-tpt-iter1-r64
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
robotgeneralist/openpi-nomagic | robotgeneralist | 2025-06-12T14:24:38Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-04-14T18:14:58Z | ---
license: mit
---
# Nomagic Simple / Adversarial Box Model Checkpoints
This is a repo to store the most important checkpoints of the `openpi` model.
## Uploading checkpoints
Since the checkpoints are huge,
the fastest and most reliable way to upload them
is by using the `upload-large-folder` command from `huggingface-cli`.
To do so, you first have to log in with appropriate credentials
(you need a token with write permissions to the target repository):
```
huggingface-cli login
```
Next, use `upload-large-folder`.
For example, to upload the `checkpoints` directory to the remote repository, run:
```
huggingface-cli upload-large-folder robotgeneralist/openpi-nomagic-multibox checkpoints --repo-type=model
```
Note that there is no way to specify a target path where the data will be stored on the remote.
The contents of the directory will be placed under the root directory.
So, for example, if your local folder is organized like the following:
```
checkpoints
--some-dir
--file1
--file2
```
after uploading to the remote, you will have:
```
some-dir
--file1
--file2
```
Luckily, you can still upload additional files later on.
For example, if after the first upload you try to upload:
```
checkpoints
--some-dir
--file3
--file4
```
the remote will become:
```
some-dir
--file1
--file2
--file3
--file4
```
Hence, even though slightly inconvenient,
this seems to be the best method for uploading big checkpoints,
because of its efficiency and robustness.
|
ArseleneB/tinyllama-fitness-chatbot-cpu-v1 | ArseleneB | 2025-06-12T14:13:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T12:52:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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phospho-app/oulianov-ACT_BBOX-TEST10-m2yj9 | phospho-app | 2025-06-12T14:03:41Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T13:45:05Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Training process failed with exit code 1:
'timestamps': [np.float32(7.0666666), np.float32(0.0)]},
{'diff': np.float32(-6.4333334),
'episode_index': 33,
'timestamps': [np.float32(6.4333334), np.float32(0.0)]},
{'diff': np.float32(-5.9666667),
'episode_index': 34,
'timestamps': [np.float32(5.9666667), np.float32(0.0)]},
{'diff': np.float32(-6.2),
'episode_index': 35,
'timestamps': [np.float32(6.2), np.float32(0.0)]}]
```
## Training parameters:
- **Dataset**: [phospho-app/TEST10_bboxes](https://huggingface.co/datasets/phospho-app/TEST10_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
LandCruiser/sn29_june_12_3 | LandCruiser | 2025-06-12T14:00:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T13:39:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BBoDDoGood/SLM | BBoDDoGood | 2025-06-12T13:48:17Z | 16 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:adapter:facebook/bart-base",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T02:53:37Z | ---
library_name: peft
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: SLM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SLM
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0
- Datasets 3.6.0
- Tokenizers 0.21.1 |
profientw3456/inceptionv3model-wildlife | profientw3456 | 2025-06-12T13:44:27Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T11:10:27Z | # Inceptionv3Model Wildlife
Production-ready wildlife detection model using Inception v3 + Faster R-CNN, optimized for real-world API deployment.
## Model Details
- **Architecture**: Inception v3 + Faster R-CNN
- **Framework**: Detectron2 + PyTorch
- **Classes**: 11 wildlife animals
- **Pretrained**: Yes (Inception v3 backbone)
- **Upload Date**: 2025-06-12
## Detected Animals
- Antelope, Buffalo, Elephant, Giraffe, Gorilla, Hippopotamus, Leopard, Lion, Rhino, Wolf, Zebra
## Quick Start
```python
from inference_api import WildlifeDetectorAPI
detector = WildlifeDetectorAPI(
model_path="model_final.pth",
config_path="production_config.json"
)
result = detector.predict("image.jpg", confidence_threshold=0.5)
print(result)
```
## API Response Format
```json
{
"success": true,
"detections": [
{
"class_id": 0,
"class_name": "antelope",
"confidence": 0.85,
"confidence_level": "high",
"bbox": {
"x1": 100.0,
"y1": 200.0,
"x2": 300.0,
"y2": 450.0,
"width": 200.0,
"height": 250.0
}
}
],
"summary": {
"total_detections": 1,
"high_confidence": 1,
"medium_confidence": 0,
"low_confidence": 0
}
}
```
## Installation
```bash
pip install -r requirements.txt
# Install detectron2 (platform-specific, example for CUDA 11.3, PyTorch 1.10)
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
```
## Files Included
- `model_final.pth`: Trained model weights
- `production_config.json`: Production configuration
- `inference_api.py`: Inference script for API integration
- `requirements.txt`: Dependencies
## Confidence Levels
- **High**: ≥ 80%
- **Medium**: 60-79%
- **Low**: 40-59%
## Production Tips
- Use GPU for faster inference.
- Set confidence threshold based on application needs.
- Cache model instance for API performance.
- Handle invalid image inputs gracefully.
## License
Apache-2.0 (ensure compliance with your dataset).
|
maazarif12/dpo_safe_medium | maazarif12 | 2025-06-12T13:42:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T13:42:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
InfoTokenizers/fw57M-tied_finewebedu-20B_BPE_64000 | InfoTokenizers | 2025-06-12T13:37:47Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | 2025-06-12T13:22:56Z | ---
{}
---
## Experiment Configuration
```yaml
callbacks:
grad_accum:
_target_: src.callbacks.gradient_accumulation.GradientAccumulationScheduler
scheduling:
0: 2
grad_norm:
_target_: src.callbacks.grad_norm.GradNorm
check_clipping: false
group_separator: /
histogram_freq: null
log_weight_distribution: false
norm_type: 2
only_total: true
lr_monitor:
_target_: src.callbacks.lr_monitor.SimpleLearningRateMonitor
model_checkpoint:
_target_: src.callbacks.model_checkpoint.ModelCheckpoint
dirpath: .checkpoints
enable_version_counter: false
every_n_train_steps: 2000
filename: '{step}'
save_initial_checkpoint: true
save_last: link
save_top_k: -1
verbose: true
speed_monitor:
_target_: src.callbacks.speed_monitor.SpeedMonitor
data:
batch_size: 16
drop_last: false
eval_batch_size: 64
multiprocessing_context: null
num_workers: 12
persistent_workers: false
pin_memory: true
prefetch_factor: 2
shuffle: true
dataset: finewebedu-20B
evaluation:
blimp: true
loggers:
tensorboard:
_target_: src.trainer.TensorBoardLogger
name: ''
save_dir: ./
version: null
model: fw57M-tied
optim:
lr: 0.0006
num_warmup_steps: 2000
optim_kwargs:
betas:
- 0.9
- 0.95
eps: 1.0e-08
fused: true
optim_name: adamw
scheduler_kwargs:
min_lr_ratio: 0.01
num_decay_steps: 4000
num_stable_steps: 44000
scheduler_name: warmup_stable_decay
weight_decay: 0.01
out_parent_folder: model_train
pwd: /home/zg258/rds/hpc-work/infotokenization
resume_from_checkpoint: .checkpoints/last.ckpt
run_folder: .
save_initial_checkpoint: true
seed: 42
tok_name: frequency_64000
torch_compile: true
train_data_path: /home/zg258/rds/hpc-work/infotokenization/data/finewebedu-20B/frequency_64000/train
trainer:
accelerator: gpu
deterministic: false
devices: 4
enable_progress_bar: true
fast_dev_run: false
gradient_clip_algorithm: norm
gradient_clip_val: 1.0
limit_val_batches: 500
log_every_n_steps: 1
max_steps: 50000
precision: bf16-true
val_check_interval: 2000
val_data_path: /home/zg258/rds/hpc-work/infotokenization/data/finewebedu-20B/frequency_64000/validation
``` |
Aeshp/deepseekR1iitgdata | Aeshp | 2025-06-12T13:30:19Z | 0 | 0 | null | [
"safetensors",
"unsloth",
"en",
"base_model:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit",
"license:mit",
"region:us"
] | null | 2025-06-12T13:17:26Z | ---
license: mit
language:
- en
base_model:
- unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit
new_version: Aeshp/deepseekR1iitgdata
tags:
- unsloth
---
|
morturr/Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-7-2025-06-12 | morturr | 2025-06-12T13:28:25Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T13:28:10Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-7-2025-06-12
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1-PAIR_one_liners_amazon-COMB-one_liners-comb-2-seed-7-2025-06-12
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
phospho-app/oulianov-ACT_BBOX-TEST10-mefu1 | phospho-app | 2025-06-12T13:28:24Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T13:27:24Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Parquet file /__modal/volumes/vo-5orm28H7kowpsUapdIMyip/datasets/Lithium73fr/TEST10_bboxes/data/chunk-000/episode_000036.parquet does not contain 'observation.environment_state' key. This is unexpected after computing bounding boxes.
```
## Training parameters:
- **Dataset**: [Lithium73fr/TEST10](https://huggingface.co/datasets/Lithium73fr/TEST10)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
arinnnnn/peft_lora_t5_small_v1.2 | arinnnnn | 2025-06-12T13:22:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T13:21:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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abir-hr196/gpt2-hf_multilingual-90 | abir-hr196 | 2025-06-12T13:17:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T13:12:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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mradermacher/arvox-turn-4B-GGUF | mradermacher | 2025-06-12T13:16:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:shahink/arvox-turn-4B",
"base_model:quantized:shahink/arvox-turn-4B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T12:43:27Z | ---
base_model: shahink/arvox-turn-4B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shahink/arvox-turn-4B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-4B-GGUF/resolve/main/arvox-turn-4B.f16.gguf) | f16 | 8.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Vacaspati/VAC-BERT | Vacaspati | 2025-06-12T13:15:08Z | 25 | 0 | null | [
"pytorch",
"electra",
"bn",
"base_model:google/electra-small-discriminator",
"base_model:finetune:google/electra-small-discriminator",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T18:11:44Z | ---
license: apache-2.0
language:
- bn
base_model:
- google/electra-small-discriminator
---
# VĀC-BERT
**VĀC-BERT** is a 17 million-parameter model, trained on the Vācaspati literary dataset. Despite its compact size, VĀC-BERT achieves competitive performance with state-of-the-art masked-language and downstream models that are over seven times larger.
## Model Details
- **Architecture:** Electra-small (but reduced to 17 M parameters)
- **Pretraining Corpus:** Vācaspati — a curated Bangla literary corpus
- **Parameter Count:** 17 M (≈ 1/7th the size of BERT-base)
- **Tokenizer:** WordPiece, vocabulary size 50 K
## Usage Example
```python
from transformers import BertTokenizer, AutoModelForSequenceClassification
tokenizer = BertTokenizer.from_pretrained("Vacaspati/VAC-BERT")
model = AutoModelForSequenceClassification.from_pretrained("Vacaspati/VAC-BERT")
```
## Citation
If you are using this model please cite:
```bibtex
@inproceedings{bhattacharyya-etal-2023-vacaspati,
title = "{VACASPATI}: A Diverse Corpus of {B}angla Literature",
author = "Bhattacharyya, Pramit and
Mondal, Joydeep and
Maji, Subhadip and
Bhattacharya, Arnab",
editor = "Park, Jong C. and
Arase, Yuki and
Hu, Baotian and
Lu, Wei and
Wijaya, Derry and
Purwarianti, Ayu and
Krisnadhi, Adila Alfa",
booktitle = "Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = nov,
year = "2023",
address = "Nusa Dua, Bali",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.ijcnlp-main.72/",
doi = "10.18653/v1/2023.ijcnlp-main.72",
pages = "1118--1130"
}
``` |
OctoLong/LLamaChain-32k-mix | OctoLong | 2025-06-12T13:14:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T13:10:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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robinfaro/molm-router-fineweb_edu-100BT | robinfaro | 2025-06-12T13:05:17Z | 45 | 0 | transformers | [
"transformers",
"safetensors",
"molm",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-06-11T17:06:30Z | ---
library_name: transformers
tags: []
---
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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aieng-lab/Llama-3.2-1B_closed-question | aieng-lab | 2025-06-12T12:59:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"en",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T12:58:19Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-classification
---
# Llama 3.2 1b for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
aieng-lab/gpt2-xl_closed-question | aieng-lab | 2025-06-12T12:56:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-xl",
"base_model:finetune:openai-community/gpt2-xl",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T12:55:19Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-xl
pipeline_tag: text-classification
---
# GPT-2 xl for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [gpt2-xl](https://huggingface.co/gpt2-xl)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
Ulhume/tagazok1 | Ulhume | 2025-06-12T12:56:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T15:46:47Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ulhume
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tomaarsen/splade-distilbert-base-uncased-nq | tomaarsen | 2025-06-12T12:55:30Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"distilbert",
"sparse-encoder",
"sparse",
"splade",
"generated_from_trainer",
"dataset_size:99000",
"loss:SpladeLoss",
"loss:SparseMultipleNegativesRankingLoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2205.04733",
"arxiv:1705.00652",
"arxiv:2004.05665",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-12T12:55:20Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
base_model: distilbert/distilbert-base-uncased
widget:
- text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
of the former World Trade Center in New York City. The introduction features Ben
Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the
keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles.
The rest of the video has several cuts to Durst and his bandmates hanging out
of the Bentley as they drive about Manhattan. The song Ben Stiller is playing
at the beginning is "My Generation" from the same album. The video also features
scenes of Fred Durst with five girls dancing in a room. The video was filmed around
the same time as the film Zoolander, which explains Stiller and Dorff's appearance.
Fred Durst has a small cameo in that film.
- text: 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release
date once again, to February 9, 2018, in order to allow more time for post-production;
months later, on August 25, the studio moved the release forward two weeks.[17]
The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]'
- text: who played the dj in the movie the warriors
- text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with
a growth hormone deficiency as a child. At age 13, he relocated to Spain to join
Barcelona, who agreed to pay for his medical treatment. After a fast progression
through Barcelona's youth academy, Messi made his competitive debut aged 17 in
October 2004. Despite being injury-prone during his early career, he established
himself as an integral player for the club within the next three years, finishing
2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year
award, a feat he repeated the following year. His first uninterrupted campaign
came in the 2008–09 season, during which he helped Barcelona achieve the first
treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA
World Player of the Year award by record voting margins.
- text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim
for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s
film Smiles of a Summer Night. It is a ballad from Act Two, in which the character
Desirée reflects on the ironies and disappointments of her life. Among other things,
she looks back on an affair years earlier with the lawyer Fredrik, who was deeply
in love with her but whose marriage proposals she had rejected. Meeting him after
so long, she realizes she is in love with him and finally ready to marry him,
but now it is he who rejects her: he is in an unconsummated marriage with a much
younger woman. Desirée proposes marriage to rescue him from this situation, but
he declines, citing his dedication to his bride. Reacting to his rejection, Desirée
sings this song. The song is later reprised as a coda after Fredrik''s young wife
runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 36.35355068873359
energy_consumed: 0.0935255045992395
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.252
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: DistilBERT base trained on Natural-Questions tuples
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.4
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.74
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.13333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.124
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
name: Dot Recall@1
- type: dot_recall@3
value: 0.4
name: Dot Recall@3
- type: dot_recall@5
value: 0.62
name: Dot Recall@5
- type: dot_recall@10
value: 0.74
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.48417691239896954
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.40474603174603174
name: Dot Mrr@10
- type: dot_map@100
value: 0.4165931820854422
name: Dot Map@100
- type: query_active_dims
value: 68.80000305175781
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9977458881117962
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 135.5758514404297
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9955580941143952
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.42
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.52
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.56
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.42
name: Dot Precision@1
- type: dot_precision@3
value: 0.36
name: Dot Precision@3
- type: dot_precision@5
value: 0.308
name: Dot Precision@5
- type: dot_precision@10
value: 0.234
name: Dot Precision@10
- type: dot_recall@1
value: 0.024688245739830684
name: Dot Recall@1
- type: dot_recall@3
value: 0.05757259881654739
name: Dot Recall@3
- type: dot_recall@5
value: 0.07457503506379409
name: Dot Recall@5
- type: dot_recall@10
value: 0.09455914797791706
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.2854029431260111
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.46341269841269833
name: Dot Mrr@10
- type: dot_map@100
value: 0.11792914877304508
name: Dot Map@100
- type: query_active_dims
value: 79.31999969482422
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9974012188030004
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 184.8435516357422
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9939439240011879
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.4
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.72
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.76
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.4
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14400000000000002
name: Dot Precision@5
- type: dot_precision@10
value: 0.07600000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.38
name: Dot Recall@1
- type: dot_recall@3
value: 0.61
name: Dot Recall@3
- type: dot_recall@5
value: 0.68
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.562112822249959
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.535079365079365
name: Dot Mrr@10
- type: dot_map@100
value: 0.5164611300715877
name: Dot Map@100
- type: query_active_dims
value: 54.099998474121094
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9982275080769897
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 133.11419677734375
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9956387459282701
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.3666666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5133333333333333
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.62
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6866666666666666
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3666666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.23555555555555552
name: Dot Precision@3
- type: dot_precision@5
value: 0.19200000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.12800000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.22822941524661022
name: Dot Recall@1
- type: dot_recall@3
value: 0.35585753293884914
name: Dot Recall@3
- type: dot_recall@5
value: 0.45819167835459806
name: Dot Recall@5
- type: dot_recall@10
value: 0.511519715992639
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4438975592583132
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.46774603174603174
name: Dot Mrr@10
- type: dot_map@100
value: 0.350327820310025
name: Dot Map@100
- type: query_active_dims
value: 67.4066670735677
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9977915383305954
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 145.78942579758726
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9952234641963965
name: Corpus Sparsity Ratio
---
# DistilBERT base trained on Natural-Questions tuples
This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** SPLADE Sparse Encoder
- **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq")
# Run inference
queries = [
"is send in the clowns from a musical",
]
documents = [
'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
'Money in the Bank ladder match The first match was contested in 2005 at WrestleMania 21, after being invented (in kayfabe) by Chris Jericho.[1] At the time, it was exclusive to wrestlers of the Raw brand, and Edge won the inaugural match.[1] From then until 2010, the Money in the Bank ladder match, now open to all WWE brands, became a WrestleMania mainstay. 2010 saw a second and third Money in the Bank ladder match when the Money in the Bank pay-per-view debuted in July. Unlike the matches at WrestleMania, this new event featured two such ladder matches – one each for a contract for the WWE Championship and World Heavyweight Championship, respectively.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[27.6088, 3.8288, 3.8780]])
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:----------------------|:------------|:-------------|:-----------|
| dot_accuracy@1 | 0.28 | 0.42 | 0.4 |
| dot_accuracy@3 | 0.4 | 0.5 | 0.64 |
| dot_accuracy@5 | 0.62 | 0.52 | 0.72 |
| dot_accuracy@10 | 0.74 | 0.56 | 0.76 |
| dot_precision@1 | 0.28 | 0.42 | 0.4 |
| dot_precision@3 | 0.1333 | 0.36 | 0.2133 |
| dot_precision@5 | 0.124 | 0.308 | 0.144 |
| dot_precision@10 | 0.074 | 0.234 | 0.076 |
| dot_recall@1 | 0.28 | 0.0247 | 0.38 |
| dot_recall@3 | 0.4 | 0.0576 | 0.61 |
| dot_recall@5 | 0.62 | 0.0746 | 0.68 |
| dot_recall@10 | 0.74 | 0.0946 | 0.7 |
| **dot_ndcg@10** | **0.4842** | **0.2854** | **0.5621** |
| dot_mrr@10 | 0.4047 | 0.4634 | 0.5351 |
| dot_map@100 | 0.4166 | 0.1179 | 0.5165 |
| query_active_dims | 68.8 | 79.32 | 54.1 |
| query_sparsity_ratio | 0.9977 | 0.9974 | 0.9982 |
| corpus_active_dims | 135.5759 | 184.8436 | 133.1142 |
| corpus_sparsity_ratio | 0.9956 | 0.9939 | 0.9956 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3667 |
| dot_accuracy@3 | 0.5133 |
| dot_accuracy@5 | 0.62 |
| dot_accuracy@10 | 0.6867 |
| dot_precision@1 | 0.3667 |
| dot_precision@3 | 0.2356 |
| dot_precision@5 | 0.192 |
| dot_precision@10 | 0.128 |
| dot_recall@1 | 0.2282 |
| dot_recall@3 | 0.3559 |
| dot_recall@5 | 0.4582 |
| dot_recall@10 | 0.5115 |
| **dot_ndcg@10** | **0.4439** |
| dot_mrr@10 | 0.4677 |
| dot_map@100 | 0.3503 |
| query_active_dims | 67.4067 |
| query_sparsity_ratio | 0.9978 |
| corpus_active_dims | 145.7894 |
| corpus_sparsity_ratio | 0.9952 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 3e-05,
"lambda_query": 5e-05
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0323 | 200 | 139.5463 | - | - | - | - | - |
| 0.0646 | 400 | 0.3152 | - | - | - | - | - |
| 0.0970 | 600 | 0.1291 | - | - | - | - | - |
| 0.1293 | 800 | 0.0783 | - | - | - | - | - |
| 0.1616 | 1000 | 0.0311 | 0.0839 | 0.4749 | 0.2698 | 0.5106 | 0.4184 |
| 0.1939 | 1200 | 0.0427 | - | - | - | - | - |
| 0.2262 | 1400 | 0.0368 | - | - | - | - | - |
| 0.2586 | 1600 | 0.042 | - | - | - | - | - |
| 0.2909 | 1800 | 0.0384 | - | - | - | - | - |
| 0.3232 | 2000 | 0.0429 | 0.0632 | 0.4251 | 0.2626 | 0.5297 | 0.4058 |
| 0.3555 | 2200 | 0.0304 | - | - | - | - | - |
| 0.3878 | 2400 | 0.0357 | - | - | - | - | - |
| 0.4202 | 2600 | 0.0294 | - | - | - | - | - |
| 0.4525 | 2800 | 0.0289 | - | - | - | - | - |
| 0.4848 | 3000 | 0.0287 | 0.0563 | 0.4496 | 0.2417 | 0.5590 | 0.4168 |
| 0.5171 | 3200 | 0.0269 | - | - | - | - | - |
| 0.5495 | 3400 | 0.0395 | - | - | - | - | - |
| 0.5818 | 3600 | 0.0191 | - | - | - | - | - |
| 0.6141 | 3800 | 0.0328 | - | - | - | - | - |
| 0.6464 | 4000 | 0.0295 | 0.0502 | 0.4882 | 0.2537 | 0.5795 | 0.4405 |
| 0.6787 | 4200 | 0.0155 | - | - | - | - | - |
| 0.7111 | 4400 | 0.0274 | - | - | - | - | - |
| 0.7434 | 4600 | 0.0324 | - | - | - | - | - |
| 0.7757 | 4800 | 0.0197 | - | - | - | - | - |
| 0.8080 | 5000 | 0.0178 | 0.0417 | 0.4871 | 0.2599 | 0.5651 | 0.4374 |
| 0.8403 | 5200 | 0.0296 | - | - | - | - | - |
| 0.8727 | 5400 | 0.0194 | - | - | - | - | - |
| 0.9050 | 5600 | 0.0235 | - | - | - | - | - |
| 0.9373 | 5800 | 0.0191 | - | - | - | - | - |
| 0.9696 | 6000 | 0.0173 | 0.0390 | 0.4837 | 0.2866 | 0.5574 | 0.4425 |
| -1 | -1 | - | - | 0.4842 | 0.2854 | 0.5621 | 0.4439 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.094 kWh
- **Carbon Emitted**: 0.036 kg of CO2
- **Hours Used**: 0.252 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
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aieng-lab/bert-base-cased_closed-question | aieng-lab | 2025-06-12T12:47:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-12T12:47:00Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- bert-base-cased
pipeline_tag: text-classification
---
# BERT base for classifying developer questions
This model classifies questions in developer forums (e.g., Stack Overflow) as 'open', 'not a real question', 'off topic', 'not constructive' or 'too localized'.
- **Developed by:** Fabian C. Peña, Steffen Herbold
- **Finetuned from:** [bert-base-cased](https://huggingface.co/bert-base-cased)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peña and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
louijiec/gluonts-chronosformer | louijiec | 2025-06-12T12:41:21Z | 0 | 0 | null | [
"safetensors",
"time_series_transformer",
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T12:28:15Z | ---
license: apache-2.0
---
|
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.15_0.15_0.05_epoch1 | MinaMila | 2025-06-12T12:40:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T12:38:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
RAIL-KNUST/ct-mri | RAIL-KNUST | 2025-06-12T12:35:39Z | 0 | 0 | null | [
"medical",
"biology",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-11T14:24:32Z | ---
license: apache-2.0
language:
- en
metrics:
- accuracy
tags:
- medical
- biology
--- |
CapstoneML/HistoryLensModel | CapstoneML | 2025-06-12T12:35:30Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-12T12:35:27Z | ---
license: apache-2.0
---
|
bbunzeck/german-babylm-pjg-char | bbunzeck | 2025-06-12T12:14:29Z | 0 | 0 | null | [
"text-generation",
"de",
"dataset:bbunzeck/babylm-german",
"region:us"
] | text-generation | 2025-05-28T12:22:50Z | ---
datasets:
- bbunzeck/babylm-german
language:
- de
pipeline_tag: text-generation
--- |
phospho-app/PAphospho-ACT_BBOX-circle-box-bbact-1006000 | phospho-app | 2025-06-12T12:13:09Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-12T11:41:29Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/circle-box-bbact_bboxes](https://huggingface.co/datasets/phospho-app/circle-box-bbact_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 6000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
19uez/model_llama3_2_3B_64_0_5k_SFT | 19uez | 2025-06-12T12:11:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T12:09:44Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** 19uez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Pokrovec/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_scavenging_sparrow | Pokrovec | 2025-06-12T12:03:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prehistoric scavenging sparrow",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T05:11:31Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_scavenging_sparrow
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prehistoric scavenging sparrow
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_scavenging_sparrow
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Pokrovec/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-prehistoric_scavenging_sparrow", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MinaMila/phi3_unlearned_2nd_1e-6_1.0_0.15_0.25_0.15_epoch1 | MinaMila | 2025-06-12T11:55:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T11:53:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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Jeff13680/whisper-large-v3-lora-finetuned-merged | Jeff13680 | 2025-06-12T11:53:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-12T11:28:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
MaiAhmed/medgemma-4b-it-sft-lora-flare-classification | MaiAhmed | 2025-06-12T11:46:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T00:59:33Z | ---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-flare-classification
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-flare-classification
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="MaiAhmed/medgemma-4b-it-sft-lora-flare-classification", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mai-cs/huggingface/runs/fwwxohzh)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.51.3
- Pytorch: 2.3.1+cu118
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/arvox-turn-1.7B-GGUF | mradermacher | 2025-06-12T11:39:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:shahink/arvox-turn-1.7B",
"base_model:quantized:shahink/arvox-turn-1.7B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T11:28:22Z | ---
base_model: shahink/arvox-turn-1.7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/shahink/arvox-turn-1.7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/arvox-turn-1.7B-GGUF/resolve/main/arvox-turn-1.7B.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Information5710/salsa | Information5710 | 2025-06-12T11:39:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T11:37:58Z | Watch 🟢 ➤ ➤ ➤ 🌐<a href="https://subwayreh.cfd/szaqwsd">Full Original Video bu guru salsa bu guru salsa tiktok) |
Rmie/Spatial_ft | Rmie | 2025-06-12T11:36:28Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llava_llama",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-12T10:58:21Z | ---
# base_model: "/root/NL/checkpoints"
base_model: "spa"
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.9.0 |
prasannabadiger7/ner-bert-model | prasannabadiger7 | 2025-06-12T11:29:14Z | 0 | 1 | null | [
"safetensors",
"bert",
"en",
"dataset:tner/conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:mit",
"region:us"
] | null | 2025-06-10T15:43:58Z | ---
license: mit
datasets:
- tner/conll2003
language:
- en
base_model:
- google-bert/bert-base-cased
--- |
numind/NuExtract-2.0-4B | numind | 2025-06-12T11:24:46Z | 155 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"conversational",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-26T08:22:58Z | ---
library_name: transformers
license: mit
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
---
<p align="center">
<img src="https://cdn.prod.website-files.com/638364a4e52e440048a9529c/64188f405afcf42d0b85b926_logo_numind_final.png" width="200"/>
<p>
<p align="center">
🖥️ <a href="https://nuextract.ai/">API / Platform</a>   |   📑 <a href="https://numind.ai/blog">Blog</a>   |   🗣️ <a href="https://discord.gg/3tsEtJNCDe">Discord</a>
</p>
# NuExtract 2.0 4B by NuMind 📈📈📈
NuExtract 2.0 is a family of models trained specifically for structured information extraction tasks. It supports both multimodal inputs and is multilingual.
We provide several versions of different sizes, all based on pre-trained models from the QwenVL family.
| Model Size | Model Name | Base Model | License | Huggingface Link |
|------------|------------|------------|---------|------------------|
| 2B | NuExtract-2.0-2B | [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) | MIT | 🤗 [NuExtract-2.0-2B](https://huggingface.co/numind/NuExtract-2.0-2B) |
| 4B | NuExtract-2.0-4B | [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) | Qwen Research License | 🤗 [NuExtract-2.0-4B](https://huggingface.co/numind/NuExtract-2.0-4B) |
| 8B | NuExtract-2.0-8B | [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) | MIT | 🤗 [NuExtract-2.0-8B](https://huggingface.co/numind/NuExtract-2.0-8B) |
❗️Note: `NuExtract-2.0-2B` is based on Qwen2-VL rather than Qwen2.5-VL because the smallest Qwen2.5-VL model (3B) has a more restrictive, non-commercial license. We therefore include `NuExtract-2.0-2B` as a small model option that can be used commercially.
## Overview
To use the model, provide an input text/image and a JSON template describing the information you need to extract. The template should be a JSON object, specifying field names and their expected type.
Support types include:
* `verbatim-string` - instructs the model to extract text that is present verbatim in the input.
* `string` - a generic string field that can incorporate paraphrasing/abstraction.
* `integer` - a whole number.
* `number` - a whole or decimal number.
* `date-time` - ISO formatted date.
* Array of any of the above types (e.g. `["string"]`)
* `enum` - a choice from set of possible answers (represented in template as an array of options, e.g. `["yes", "no", "maybe"]`).
* `multi-label` - an enum that can have multiple possible answers (represented in template as a double-wrapped array, e.g. `[["A", "B", "C"]]`).
If the model does not identify relevant information for a field, it will return `null` or `[]` (for arrays and multi-labels).
The following is an example template:
```json
{
"first_name": "verbatim-string",
"last_name": "verbatim-string",
"description": "string",
"age": "integer",
"gpa": "number",
"birth_date": "date-time",
"nationality": ["France", "England", "Japan", "USA", "China"],
"languages_spoken": [["English", "French", "Japanese", "Mandarin", "Spanish"]]
}
```
An example output:
```json
{
"first_name": "Susan",
"last_name": "Smith",
"description": "A student studying computer science.",
"age": 20,
"gpa": 3.7,
"birth_date": "2005-03-01",
"nationality": "England",
"languages_spoken": ["English", "French"]
}
```
⚠️ We recommend using NuExtract with a temperature at or very close to 0. Some inference frameworks, such as Ollama, use a default of 0.7 which is not well suited to many extraction tasks.
## Using NuExtract with 🤗 Transformers
```python
import torch
from transformers import AutoProcessor, AutoModelForVision2Seq
model_name = "numind/NuExtract-2.0-2B"
# model_name = "numind/NuExtract-2.0-8B"
model = AutoModelForVision2Seq.from_pretrained(model_name,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map="auto")
processor = AutoProcessor.from_pretrained(model_name,
trust_remote_code=True,
padding_side='left',
use_fast=True)
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained(model_name, min_pixels=min_pixels, max_pixels=max_pixels)
```
You will need the following function to handle loading of image input data:
```python
def process_all_vision_info(messages, examples=None):
"""
Process vision information from both messages and in-context examples, supporting batch processing.
Args:
messages: List of message dictionaries (single input) OR list of message lists (batch input)
examples: Optional list of example dictionaries (single input) OR list of example lists (batch)
Returns:
A flat list of all images in the correct order:
- For single input: example images followed by message images
- For batch input: interleaved as (item1 examples, item1 input, item2 examples, item2 input, etc.)
- Returns None if no images were found
"""
from qwen_vl_utils import process_vision_info, fetch_image
# Helper function to extract images from examples
def extract_example_images(example_item):
if not example_item:
return []
# Handle both list of examples and single example
examples_to_process = example_item if isinstance(example_item, list) else [example_item]
images = []
for example in examples_to_process:
if isinstance(example.get('input'), dict) and example['input'].get('type') == 'image':
images.append(fetch_image(example['input']))
return images
# Normalize inputs to always be batched format
is_batch = messages and isinstance(messages[0], list)
messages_batch = messages if is_batch else [messages]
is_batch_examples = examples and isinstance(examples, list) and (isinstance(examples[0], list) or examples[0] is None)
examples_batch = examples if is_batch_examples else ([examples] if examples is not None else None)
# Ensure examples batch matches messages batch if provided
if examples and len(examples_batch) != len(messages_batch):
if not is_batch and len(examples_batch) == 1:
# Single example set for a single input is fine
pass
else:
raise ValueError("Examples batch length must match messages batch length")
# Process all inputs, maintaining correct order
all_images = []
for i, message_group in enumerate(messages_batch):
# Get example images for this input
if examples and i < len(examples_batch):
input_example_images = extract_example_images(examples_batch[i])
all_images.extend(input_example_images)
# Get message images for this input
input_message_images = process_vision_info(message_group)[0] or []
all_images.extend(input_message_images)
return all_images if all_images else None
```
E.g. To perform a basic extraction of names from a text document:
```python
template = """{"names": ["string"]}"""
document = "John went to the restaurant with Mary. James went to the cinema."
# prepare the user message content
messages = [{"role": "user", "content": document}]
text = processor.tokenizer.apply_chat_template(
messages,
template=template, # template is specified here
tokenize=False,
add_generation_prompt=True,
)
print(text)
""""<|im_start|>user
# Template:
{"names": ["string"]}
# Context:
John went to the restaurant with Mary. James went to the cinema.<|im_end|>
<|im_start|>assistant"""
image_inputs = process_all_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
# we choose greedy sampling here, which works well for most information extraction tasks
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
# Inference: Generation of the output
generated_ids = model.generate(
**inputs,
**generation_config
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# ['{"names": ["John", "Mary", "James"]}']
```
<details>
<summary>In-Context Examples</summary>
Sometimes the model might not perform as well as we want because our task is challenging or involves some degree of ambiguity. Alternatively, we may want the model to follow some specific formatting, or just give it a bit more help. In cases like this it can be valuable to provide "in-context examples" to help NuExtract better understand the task.
To do so, we can provide a list examples (dictionaries of input/output pairs). In the example below, we show to the model that we want the extracted names to be in captial letters with `-` on either side (for the sake of illustration). Usually providing multiple examples will lead to better results.
```python
template = """{"names": ["string"]}"""
document = "John went to the restaurant with Mary. James went to the cinema."
examples = [
{
"input": "Stephen is the manager at Susan's store.",
"output": """{"names": ["-STEPHEN-", "-SUSAN-"]}"""
}
]
messages = [{"role": "user", "content": document}]
text = processor.tokenizer.apply_chat_template(
messages,
template=template,
examples=examples, # examples provided here
tokenize=False,
add_generation_prompt=True,
)
image_inputs = process_all_vision_info(messages, examples)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
# we choose greedy sampling here, which works well for most information extraction tasks
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
# Inference: Generation of the output
generated_ids = model.generate(
**inputs,
**generation_config
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# ['{"names": ["-JOHN-", "-MARY-", "-JAMES-"]}']
```
</details>
<details>
<summary>Image Inputs</summary>
If we want to give image inputs to NuExtract, instead of text, we simply provide a dictionary specifying the desired image file as the message content, instead of a string. (e.g. `{"type": "image", "image": "file://image.jpg"}`).
You can also specify an image URL (e.g. `{"type": "image", "image": "http://path/to/your/image.jpg"}`) or base64 encoding (e.g. `{"type": "image", "image": "data:image;base64,/9j/..."}`).
```python
template = """{"store": "verbatim-string"}"""
document = {"type": "image", "image": "file://1.jpg"}
messages = [{"role": "user", "content": [document]}]
text = processor.tokenizer.apply_chat_template(
messages,
template=template,
tokenize=False,
add_generation_prompt=True,
)
image_inputs = process_all_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
# Inference: Generation of the output
generated_ids = model.generate(
**inputs,
**generation_config
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# ['{"store": "Trader Joe\'s"}']
```
</details>
<details>
<summary>Batch Inference</summary>
```python
inputs = [
# image input with no ICL examples
{
"document": {"type": "image", "image": "file://0.jpg"},
"template": """{"store_name": "verbatim-string"}""",
},
# image input with 1 ICL example
{
"document": {"type": "image", "image": "file://0.jpg"},
"template": """{"store_name": "verbatim-string"}""",
"examples": [
{
"input": {"type": "image", "image": "file://1.jpg"},
"output": """{"store_name": "Trader Joe's"}""",
}
],
},
# text input with no ICL examples
{
"document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."},
"template": """{"names": ["string"]}""",
},
# text input with ICL example
{
"document": {"type": "text", "text": "John went to the restaurant with Mary. James went to the cinema."},
"template": """{"names": ["string"]}""",
"examples": [
{
"input": "Stephen is the manager at Susan's store.",
"output": """{"names": ["STEPHEN", "SUSAN"]}"""
}
],
},
]
# messages should be a list of lists for batch processing
messages = [
[
{
"role": "user",
"content": [x['document']],
}
]
for x in inputs
]
# apply chat template to each example individually
texts = [
processor.tokenizer.apply_chat_template(
messages[i], # Now this is a list containing one message
template=x['template'],
examples=x.get('examples', None),
tokenize=False,
add_generation_prompt=True)
for i, x in enumerate(inputs)
]
image_inputs = process_all_vision_info(messages, [x.get('examples') for x in inputs])
inputs = processor(
text=texts,
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
generation_config = {"do_sample": False, "num_beams": 1, "max_new_tokens": 2048}
# Batch Inference
generated_ids = model.generate(**inputs, **generation_config)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
for y in output_texts:
print(y)
# {"store_name": "WAL-MART"}
# {"store_name": "Walmart"}
# {"names": ["John", "Mary", "James"]}
# {"names": ["JOHN", "MARY", "JAMES"]}
```
</details>
<details>
<summary>Template Generation</summary>
If you want to convert existing schema files you have in other formats (e.g. XML, YAML, etc.) or start from an example, NuExtract 2.0 models can automatically generate this for you.
E.g. convert XML into a NuExtract template:
```python
xml_template = """<SportResult>
<Date></Date>
<Sport></Sport>
<Venue></Venue>
<HomeTeam></HomeTeam>
<AwayTeam></AwayTeam>
<HomeScore></HomeScore>
<AwayScore></AwayScore>
<TopScorer></TopScorer>
</SportResult>"""
messages = [
{
"role": "user",
"content": [{"type": "text", "text": xml_template}],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
image_inputs = process_all_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
generated_ids = model.generate(
**inputs,
**generation_config
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
# {
# "Date": "date-time",
# "Sport": "verbatim-string",
# "Venue": "verbatim-string",
# "HomeTeam": "verbatim-string",
# "AwayTeam": "verbatim-string",
# "HomeScore": "integer",
# "AwayScore": "integer",
# "TopScorer": "verbatim-string"
# }
```
E.g. generate a template from natural language description:
```python
description = "I would like to extract important details from the contract."
messages = [
{
"role": "user",
"content": [{"type": "text", "text": description}],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True,
)
image_inputs = process_all_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
padding=True,
return_tensors="pt",
).to("cuda")
generated_ids = model.generate(
**inputs,
**generation_config
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
# {
# "Contract": {
# "Title": "verbatim-string",
# "Description": "verbatim-string",
# "Terms": [
# {
# "Term": "verbatim-string",
# "Description": "verbatim-string"
# }
# ],
# "Date": "date-time",
# "Signatory": "verbatim-string"
# }
# }
```
</details>
## Fine-Tuning
You can find a fine-tuning tutorial notebook in the [cookbooks](https://github.com/numindai/nuextract/tree/main/cookbooks) folder of the [GitHub repo](https://github.com/numindai/nuextract/tree/main).
## vLLM Deployment
Run the command below to serve an OpenAI-compatible API:
```bash
vllm serve numind/NuExtract-2.0-8B --trust_remote_code --limit-mm-per-prompt image=6 --chat-template-content-format openai
```
If you encounter memory issues, set `--max-model-len` accordingly.
Send requests to the model as follows:
```python
import json
from openai import OpenAI
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
model="numind/NuExtract-2.0-8B",
temperature=0,
messages=[
{
"role": "user",
"content": [{"type": "text", "text": "Yesterday I went shopping at Bunnings"}],
},
],
extra_body={
"chat_template_kwargs": {
"template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4)
},
}
)
print("Chat response:", chat_response)
```
For image inputs, structure requests as shown below. Make sure to order the images in `"content"` as they appear in the prompt (i.e. any in-context examples before the main input).
```python
import base64
def encode_image(image_path):
"""
Encode the image file to base64 string
"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
base64_image = encode_image("0.jpg")
base64_image2 = encode_image("1.jpg")
chat_response = client.chat.completions.create(
model="numind/NuExtract-2.0-8B",
temperature=0,
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}}, # first ICL example image
{"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{base64_image2}"}}, # real input image
],
},
],
extra_body={
"chat_template_kwargs": {
"template": json.dumps(json.loads("""{\"store\": \"verbatim-string\"}"""), indent=4),
"examples": [
{
"input": "<image>",
"output": """{\"store\": \"Walmart\"}"""
}
]
},
}
)
print("Chat response:", chat_response)
``` |
HairThat768/guru | HairThat768 | 2025-06-12T11:24:31Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-12T11:23:37Z | 🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://subwayreh.cfd/szaqwsd">🌐(Full Original Video bu guru salsa bu guru salsa tiktok) |
chs20/FuseLIP-S-CC3M-MM | chs20 | 2025-06-12T11:17:36Z | 133 | 0 | pytorch | [
"pytorch",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"feature-extraction",
"arxiv:2506.03096",
"license:mit",
"region:us"
] | feature-extraction | 2025-05-26T09:31:55Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
pipeline_tag: feature-extraction
library_name: pytorch
license: mit
---
# FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens
The model was presented in the paper [FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens](https://arxiv.org/abs/2506.03096).
# Paper abstract
Contrastive language-image pre-training aligns the features of text-image pairs in a common latent space via distinct encoders for each modality. While this approach achieves impressive performance in several zero-shot tasks, it cannot natively handle multimodal inputs, i.e., encoding image and text into a single feature vector. As a remedy, it is common practice to use additional modules to merge the features extracted by the unimodal encoders. In this work, we present FuseLIP, an alternative architecture for multimodal embedding. Leveraging recent progress in discrete image tokenizers, we propose to use a single transformer model which operates on an extended vocabulary of text and image tokens. This early fusion approach allows the different modalities to interact at each depth of encoding and obtain richer representations compared to common late fusion. We collect new datasets for multimodal pre-training and evaluation, designing challenging tasks for multimodal encoder models. We show that FuseLIP outperforms other approaches in multimodal embedding tasks such as VQA and text-guided image transformation retrieval, while being comparable to baselines on unimodal tasks.
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: https://github.com/chs20/fuselip
- Paper: https://arxiv.org/abs/2506.03096
- Docs: https://github.com/chs20/fuselip |
LSX-UniWue/LLaMmlein_7B_chat | LSX-UniWue | 2025-06-12T11:07:48Z | 0 | 1 | peft | [
"peft",
"safetensors",
"llama",
"trl",
"sft",
"generated_from_trainer",
"de",
"dataset:LSX-UniWue/Guanako",
"dataset:FreedomIntelligence/sharegpt-deutsch",
"dataset:FreedomIntelligence/alpaca-gpt4-deutsch",
"arxiv:2411.11171",
"base_model:LSX-UniWue/LLaMmlein_7B",
"base_model:adapter:LSX-UniWue/LLaMmlein_7B",
"license:other",
"region:us"
] | null | 2025-04-07T21:11:16Z | ---
library_name: peft
base_model: LSX-UniWue/LLaMmlein_7B
tags:
- trl
- sft
- generated_from_trainer
datasets:
- LSX-UniWue/Guanako
- FreedomIntelligence/sharegpt-deutsch
- FreedomIntelligence/alpaca-gpt4-deutsch
language:
- de
license: other
---
# LLäMmlein 7B Chat
This is an early preview of our instruction-tuned 7B model, trained using limited German-language resources.
Please note that it is not the final version - we are actively working on improvements!
Find more details on our [page](https://www.informatik.uni-wuerzburg.de/datascience/projects/nlp/llammlein/) and our [preprint](arxiv.org/abs/2411.11171)!
## Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("LSX-UniWue/LLaMmlein_7B_chat")
tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein_7B_chat")
model = model.to("mps")
messages = [
{
"role": "user",
"content": "Was sind die wichtigsten Sehenswürdigkeiten von Berlin?",
},
]
chat = tokenizer.apply_chat_template(
messages,
return_tensors="pt",
add_generation_prompt=True,
).to("mps")
print(
tokenizer.decode(
model.generate(
chat,
max_new_tokens=100,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
repetition_penalty=1.1,
)[0],
skip_special_tokens=False,
)
)
``` |
potsawee/thai-vits-speaker1 | potsawee | 2025-06-12T11:07:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-06-12T11:06:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF | Triangle104 | 2025-06-12T11:04:01Z | 0 | 0 | null | [
"gguf",
"roleplay",
"storytelling",
"creative",
"character",
"narrative",
"nsfw",
"explicit",
"unaligned",
"ERP",
"Erotic",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:ReadyArt/Broken-Tutu-24B-Transgression-v2.0",
"base_model:finetune:ReadyArt/Broken-Tutu-24B-Transgression-v2.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-06-12T10:51:36Z | ---
license: apache-2.0
language:
- en
base_model: ReadyArt/Broken-Tutu-24B-Transgression-v2.0
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- roleplay
- storytelling
- creative
- character
- narrative
- nsfw
- explicit
- unaligned
- ERP
- Erotic
- llama-cpp
- gguf-my-repo
---
# Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF
This model was converted to GGUF format from [`ReadyArt/Broken-Tutu-24B-Transgression-v2.0`](https://huggingface.co/ReadyArt/Broken-Tutu-24B-Transgression-v2.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ReadyArt/Broken-Tutu-24B-Transgression-v2.0) for more details on the model.
---
This evolution of Broken-Tutu delivers unprecedented coherence with
reduced explicit content using classic "Transgression" techniques:
- 🧬 Expanded 43M Token Dataset - First ReadyArt model with multi-turn conversational data
- ✨ 100% Unslopped Dataset - New techniques used to generate the dataset with 0% slop
- ⚡ Enhanced Character Integrity - Maintains character authenticity while reducing explicit content
- 🛡️ Anti-Impersonation Guards - Never speaks or acts for the user
- 💎 Rebuilt from Ground Up - Optimized training settings for superior performance
- 📜 Direct Evolution - Leveraging the success of Broken-Tutu, we finetuned directly on top of the legendary model
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Broken-Tutu-24B-Transgression-v2.0-Q4_K_S-GGUF --hf-file broken-tutu-24b-transgression-v2.0-q4_k_s.gguf -c 2048
```
|
Agcs12/vetsafeposttrain2epoch | Agcs12 | 2025-06-12T11:01:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T10:58:10Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
ffurfaro/Titans-OLMo-1B-hf | ffurfaro | 2025-06-12T10:55:15Z | 28 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"tptt",
"text-generation",
"peft",
"trust_remote_code",
"custom_code",
"en",
"dataset:yahma/alpaca-cleaned",
"base_model:allenai/OLMo-1B-hf",
"base_model:finetune:allenai/OLMo-1B-hf",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-06-10T19:17:20Z | ---
language: en
license: apache-2.0
library_name: transformers
tags:
- tptt
- peft
- trust_remote_code
base_model: allenai/OLMo-1B-hf
datasets:
- yahma/alpaca-cleaned
---
# Titans-OLMo-1B-hf
Titanesque version of `allenai/OLMo-1B-hf` with parallel linearized attention (TPTT 😊) and PEFT.
## Model Details
- **Architecture:** TpttModel
- **Base model:** allenai/OLMo-1B-hf
- **LiZA config:** operator=delta_rule, mag=0.5
- **LoRA config:** r=8, alpha=16, dropout=0.05
- **torch_dtype:** bfloat16
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"ffurfaro/Titans-OLMo-1B-hf",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("ffurfaro/Titans-OLMo-1B-hf")
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs, skip_special_tokens=True))
```
## Training
- **Dataset:** yahma/alpaca-cleaned
- **Platform:** Kaggle
- **Hardware:** NVIDIA 2xT4
- **Batch size:** 3
- **Epochs:** 2.0
- **Learning rate (final):** 8.928571428571428e-06
- **Loss (final):** 1.2481
- **Training runtime:** 543.5342 sec
- **Samples per second:** 1.84
- **Steps per second:** 0.309
- **Total FLOPs:** 1652488667136000.0
- **Gradient norm (final):** 2.395750045776367
## Evaluation
- **Metrics:** Training loss only (no eval yet, table soon : PiQA, ARC, Hella, Wino, GSM8K, MMLU)
- **Results:** Final training loss: 1.2481
## Citation & Contact
If you use TPTT in your academic work, please cite [Furfaro](https://huggingface.co/ffurfaro). For questions or support, please open an issue on the [GitHub repository](https://github.com/fabienfrfr/tptt) or contact the maintainer.
--- |
Raderspace/RaDeR_gte_Qwen2-7B_MATH_LLMq_CoT_lexical | Raderspace | 2025-06-12T10:38:47Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"Reasoning",
"Retrieval",
"custom_code",
"en",
"dataset:Raderspace/MATH_qCoT_LLMquery_lexicalquery",
"arxiv:2505.18405",
"base_model:Alibaba-NLP/gte-Qwen2-7B-instruct",
"base_model:finetune:Alibaba-NLP/gte-Qwen2-7B-instruct",
"license:mit",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-29T08:50:42Z | ---
library_name: transformers
tags:
- Reasoning
- Retrieval
license: mit
datasets:
- Raderspace/MATH_qCoT_LLMquery_lexicalquery
language:
- en
base_model:
- Alibaba-NLP/gte-Qwen2-7B-instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
RaDeR, are a set of reasoning-based dense retrieval and reranker models trained with data derived from mathematical problem solving using large language models (LLMs).
RaDeR retrievers, trained for mathematical reasoning, effectively generalize to diverse retrieval reasoning tasks in the BRIGHT and RAR-b benchmarks, consistently outperforming strong baselines in overall performance.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** CIIR, UMass Amherst
- **Model type:** Retriever
- **Language(s):** English
- **License:** MIT
- **Finetuned from model:** Alibaba-NLP/gte-Qwen2-7B-instruct
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Debrup-61/RaDeR
- **Paper** https://huggingface.co/papers/2505.18405
## How to Get Started with the Model
Run the following code to start a server of the model with **vLLM** for fast inference.
```
vllm serve Raderspace/RaDeR_gte_Qwen2-7B_MATH_LLMq_CoT_lexical \
--task embed \
--trust-remote-code \
--override-pooler-config '{"pooling_type": "LAST", "normalize": true}' \
--gpu-memory-utilization 0.9 \
--api-key abc \
--tokenizer Alibaba-NLP/gte-Qwen2-7B-instruct \
--port 8001 \
--disable-log-requests \
--max-num-seqs 5000
```
Follow the code on [Github](https://github.com/Debrup-61/RaDeR/blob/main/models/RaDeR_retriever_server_API.py) to see how to query the retriever server.
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
The model was trained using the [MATH](https://huggingface.co/datasets/Raderspace/MATH_qCoT_LLMquery_lexicalquery) retrieval training dataset from RaDeR, containing CoT, LLMq and lexical query types.
#### Software
https://github.com/Debrup-61/RaDeR
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@misc{das2025raderreasoningawaredenseretrieval,
title={RaDeR: Reasoning-aware Dense Retrieval Models},
author={Debrup Das and Sam O' Nuallain and Razieh Rahimi},
year={2025},
eprint={2505.18405},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.18405},
}
```
## Model Card Contact
Debrup Das: [email protected]
|
dgambettaphd/M_llm2_run1_gen1_WXS_doc1000_synt64_lr1e-04_acm_FRESH | dgambettaphd | 2025-06-12T10:36:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T10:36:17Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
crimsnguts/finetuned-mistral-7b-uni | crimsnguts | 2025-06-12T10:29:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2025-06-12T10:27:54Z | ---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.10.0 |
gradientrouting-spar/gcd_syco_medical_advicekl_div_beta_kl-100_seed_42 | gradientrouting-spar | 2025-06-12T10:29:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T10:28:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
nupa22/Balls | nupa22 | 2025-06-12T10:23:29Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-06-12T10:23:29Z | ---
license: bigscience-openrail-m
---
|
aplux/SESR-M5 | aplux | 2025-06-12T10:18:22Z | 0 | 0 | null | [
"AIoT",
"QNN",
"image-to-image",
"license:other",
"region:us"
] | image-to-image | 2025-06-12T10:17:04Z | ---
license: other
license_name: aimet-model-zoo-license
license_link: https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf
pipeline_tag: image-to-image
tags:
- AIoT
- QNN
---
## SESR-M5: Super Resolution
SESR-M5 is an efficient image super-resolution model designed for real-time quality enhancement via lightweight architecture. Built on deep convolutional networks, it optimizes multi-scale feature fusion and computational efficiency, supporting 2x to 4x upscaling for mobile or edge device deployment. It balances detail reconstruction and denoising under low-resource constraints, targeting artifacts, blur, and low-light degradation in applications like real-time video enhancement, historical media restoration, and mobile photography. Challenges include model compression for portability, cross-device compatibility, and stability in dynamic scenes.
### Source model
- Input shape: 1x3x128x128
- Number of parameters: 0.32M
- Model size: 1.32M
- Output shape: 1x3x512x512
The source model can be found [here](https://github.com/quic/aimet-model-zoo/tree/develop/aimet_zoo_torch/sesr)
## Performance Reference
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## Inference & Model Conversion
Please search model by model name in [Model Farm](https://aiot.aidlux.com/en/models)
## License
- Source Model: [AIMET-MODEL-ZOO](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf)
- Deployable Model: [AIMET-MODEL-ZOO](https://github.com/quic/aimet-model-zoo/blob/develop/LICENSE.pdf) |
Thiraput01/QwenMed-1.7B-Reasoning | Thiraput01 | 2025-06-12T10:00:04Z | 0 | 0 | null | [
"safetensors",
"medical",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"license:mit",
"region:us"
] | text-generation | 2025-06-01T16:55:09Z | ---
license: mit
language:
- en
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
tags:
- medical
--- |
AndreiVoicuT/ppo-LunarLander-v2-C8 | AndreiVoicuT | 2025-06-12T09:59:46Z | 4 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-07T08:47:58Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -202.70 +/- 93.93
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo_implementation'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'AndreiVoicuT/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
andreapdr/LID-gemma-2-2b-it | andreapdr | 2025-06-12T09:54:43Z | 0 | 0 | null | [
"arxiv:2505.24523",
"region:us"
] | null | 2025-06-12T09:54:17Z | # LID-Llama: Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors
This repository will store the DPO fine-tuned models described in the paper "[`Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors`](https://arxiv.org/abs/2505.24523)".
|
gabrielegabellone/all-mini-mediterraneo-triplets | gabrielegabellone | 2025-06-12T09:53:48Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:29",
"loss:TripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-06-12T09:53:42Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:29
- loss:TripletLoss
base_model: sentence-transformers/all-MiniLM-L6-v2
widget:
- source_sentence: Where is the dump file copied from?
sentences:
- From the saved path to data/postgresql folder
- From /etc/data
- app/documents/
- source_sentence: What does cleantables.py eliminate?
sentences:
- It deletes instances of some specific models
- It deletes instances of all models
- It allows reading the head of a shapefile.
- source_sentence: What are some components mentioned in the text related to geopandas
and shapefiles?
sentences:
- how to run
- Overlay functions provided by Geopandas
- checkshapefile.py
- source_sentence: What is the purpose of using drop() in geopandas?
sentences:
- It deletes columns from a dataframe.
- It adds rows to a dataframe.
- python manage.py flow
- source_sentence: What are the exact versions of Django and django-rest-framework
needed?
sentences:
- python>=3.9
- Django==4.0
- app_db
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) <!-- at revision c9745ed1d9f207416be6d2e6f8de32d1f16199bf -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("gabrielegabellone/all-mini-mediterraneo-triplets")
# Run inference
sentences = [
'What are the exact versions of Django and django-rest-framework needed?',
'Django==4.0',
'python>=3.9',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 29 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>sentence_2</code>
* Approximate statistics based on the first 29 samples:
| | sentence_0 | sentence_1 | sentence_2 |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 15.17 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.14 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 9.07 tokens</li><li>max: 18 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 | sentence_2 |
|:--------------------------------------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| <code>What is the special command that allows loading data in a massive way?</code> | <code>flow</code> | <code>Data Merge Command</code> |
| <code>How does the path parameter work in the flow command?</code> | <code>The path parameter specifies the Excel file's location.</code> | <code>It saves data in a different folder instead of the main project folder.</code> |
| <code>Which method is used in the example to assign colors to the dataframe columns?</code> | <code>The loc method</code> | <code>Using a dictionary for color mapping</code> |
* Loss: [<code>TripletLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters:
```json
{
"distance_metric": "TripletDistanceMetric.EUCLIDEAN",
"triplet_margin": 5
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### TripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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