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stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-02 08:43:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 462
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18-Koko-Video/ULL.VIRAL.VIDEO.Koko.Viral.Video.Tutorial.Official | 18-Koko-Video | 2025-05-31T16:28:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T16:22:26Z | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://videohere.top/?koko)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?koko)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?koko) |
Zak587/lawjudge | Zak587 | 2025-05-31T16:25:52Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gemma2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T16:21:05Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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 ๐ค 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] |
shubham-gupta-video/Link.Full.18.shubham.gupta.viral.video.mms.link | shubham-gupta-video | 2025-05-31T16:20:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T16:18:51Z | [๐ CLICK HERE ๐ข==โบโบ WATCH NOW](https://videohere.top/?V=shubham-gupta)
[๐ด CLICK HERE ๐==โบโบ Download Now)](https://videohere.top/?V=shubham-gupta)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=shubham-gupta) |
BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmbce8ou7000210ozgv2ldp0x | BootesVoid | 2025-05-31T16:02:28Z | 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 | 2025-05-31T16:02:25Z | ---
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: GRMNBRNT
---
# Cm8Tb7Xkk0000Wzj24Pkk2M5G_Cmbce8Ou7000210Ozgv2Ldp0X
<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 `GRMNBRNT` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "GRMNBRNT",
"lora_weights": "https://huggingface.co/BootesVoid/cm8tb7xkk0000wzj24pkk2m5g_cmbce8ou7000210ozgv2ldp0x/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 [๐งจ 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/cm8tb7xkk0000wzj24pkk2m5g_cmbce8ou7000210ozgv2ldp0x', weight_name='lora.safetensors')
image = pipeline('GRMNBRNT').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/cm8tb7xkk0000wzj24pkk2m5g_cmbce8ou7000210ozgv2ldp0x/discussions) to add images that show off what youโve made with this LoRA.
|
cragtmp/c2 | cragtmp | 2025-05-31T16:02:27Z | 291 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct",
"region:us"
] | null | 2025-05-23T16:56:06Z | ---
base_model: meta-llama/Llama-3.2-11B-Vision-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 0.14.0 |
mlx-community/MiMo-VL-7B-RL-8bit | mlx-community | 2025-05-31T16:00:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"mlx",
"conversational",
"base_model:XiaomiMiMo/MiMo-VL-7B-RL",
"base_model:finetune:XiaomiMiMo/MiMo-VL-7B-RL",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-31T15:57:11Z | ---
license: mit
library_name: transformers
base_model:
- XiaomiMiMo/MiMo-VL-7B-RL
tags:
- mlx
---
# mlx-community/MiMo-VL-7B-RL-8bit
This model was converted to MLX format from [`XiaomiMiMo/MiMo-VL-7B-RL`]() using mlx-vlm version **0.1.26**.
Refer to the [original model card](https://huggingface.co/XiaomiMiMo/MiMo-VL-7B-RL) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model mlx-community/MiMo-VL-7B-RL-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
RandAName/Taxi | RandAName | 2025-05-31T15:38:12Z | 0 | 0 | null | [
"FrozenLake-v1",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-31T15:38:07Z | ---
tags:
- FrozenLake-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 8.03 +/- 2.53
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="RandAName/Taxi", 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"])
```
|
CHOOSEIT/MCQATEST_FFT_SciQ-E_Crazy_LoRA__checkpoint_15000__B4_2E_512T_LR1e-05_ACC4 | CHOOSEIT | 2025-05-31T15:34:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T15:33:08Z | ---
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 ๐ค 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] |
b34ux/fern-v4_lora | b34ux | 2025-05-31T15:25:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T15:24:15Z | ---
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:** b34ux
- **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)
|
pilemouse/Llama-3.2-3B-Q4_K_M-GGUF | pilemouse | 2025-05-31T15:23:14Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:quantized:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T15:22:56Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.2
extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\
\ Release Date: September 25, 2024\n\nโAgreementโ means the terms and conditions\
\ for use, reproduction, distribution and modification of the Llama Materials set\
\ forth herein.\n\nโDocumentationโ means the specifications, manuals and documentation\
\ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\nโLicenseeโ or โyouโ means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entityโs behalf),\
\ of the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\nโLlama 3.2โ\
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\ Jurisdiction. This Agreement will be governed and construed under the laws of\
\ the State of California without regard to choice of law principles, and the UN\
\ Convention on Contracts for the International Sale of Goods does not apply to\
\ this Agreement. The courts of California shall have exclusive jurisdiction of\
\ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\
\ (โ**Policy**โ). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\
\ the law or othersโ rights, including to:\n 1. Engage in, promote, generate,\
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\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
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\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
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\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 3.\
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\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 4. Collect, process, disclose, generate, or infer private or sensitive\
\ information about individuals, including information about individualsโ identity,\
\ health, or demographic information, unless you have obtained the right to do so\
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\ facilitate any action, to intentionally circumvent or remove usage restrictions\
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\ that present a risk of death or bodily harm to individuals, including use of Llama\
\ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\
\ applications, espionage, use for materials or activities that are subject to the\
\ International Traffic Arms Regulations (ITAR) maintained by the United States\
\ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\
\ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\
\ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\
\ substances\n 11. Operation of critical infrastructure, transportation technologies,\
\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\
\ and eating disorders\n 13. Any content intended to incite or promote violence,\
\ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\
\ or mislead others, including use of Llama 3.2 related to the following:\n 14.\
\ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\
\ 15. Generating, promoting, or furthering defamatory content, including the\
\ creation of defamatory statements, images, or other content\n 16. Generating,\
\ promoting, or further distributing spam\n 17. Impersonating another individual\
\ without consent, authorization, or legal right\n 18. Representing that the\
\ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\
\ false online engagement, including fake reviews and other means of fake online\
\ engagementย \n4. Fail to appropriately disclose to end users any known dangers\
\ of your AI system 5. Interact with third party tools, models, or software designed\
\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
\ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\
\ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\
\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
\ models.\n\nPlease report any violation of this Policy, software โbug,โ or other\
\ problems that could lead to a violation of this Policy through one of the following\
\ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\n\
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\n\
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\ 3.2: [email protected]"
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and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
base_model: meta-llama/Llama-3.2-3B
---
# pilemouse/Llama-3.2-3B-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.2-3B`](https://huggingface.co/meta-llama/Llama-3.2-3B) 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/meta-llama/Llama-3.2-3B) 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 pilemouse/Llama-3.2-3B-Q4_K_M-GGUF --hf-file llama-3.2-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo pilemouse/Llama-3.2-3B-Q4_K_M-GGUF --hf-file llama-3.2-3b-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 pilemouse/Llama-3.2-3B-Q4_K_M-GGUF --hf-file llama-3.2-3b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo pilemouse/Llama-3.2-3B-Q4_K_M-GGUF --hf-file llama-3.2-3b-q4_k_m.gguf -c 2048
```
|
alirezakatani/simple | alirezakatani | 2025-05-31T15:10:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-31T15:08:55Z | ---
base_model: unsloth/Phi-4-unsloth-bnb-4bit
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 0.14.0 |
BootesVoid/cmbbg4lbz06zt85uu6czzlpyo_cmbcbbqpb0ck585uunxrbz388 | BootesVoid | 2025-05-31T15:01:34Z | 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 | 2025-05-31T15:01:32Z | ---
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: sandra
---
# Cmbbg4Lbz06Zt85Uu6Czzlpyo_Cmbcbbqpb0Ck585Uunxrbz388
<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 `sandra` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "sandra",
"lora_weights": "https://huggingface.co/BootesVoid/cmbbg4lbz06zt85uu6czzlpyo_cmbcbbqpb0ck585uunxrbz388/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 [๐งจ 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/cmbbg4lbz06zt85uu6czzlpyo_cmbcbbqpb0ck585uunxrbz388', weight_name='lora.safetensors')
image = pipeline('sandra').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/cmbbg4lbz06zt85uu6czzlpyo_cmbcbbqpb0ck585uunxrbz388/discussions) to add images that show off what youโve made with this LoRA.
|
psg-vs-inter-final-on/crackstreams-pag-vs-inter-live-stream-uefa-final-tv-channel | psg-vs-inter-final-on | 2025-05-31T15:00:04Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T14:39:16Z | <a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
<a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/">๐ Viral Video Original Full HD๐ข==โบโบ WATCH NOW</a>
<a rel="nofollow" href="https://viralflix.xyz/?or">๐ด CLICK HERE ๐==โบโบ Download Now)</a> |
Westlion/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-marine_subtle_cobra | Westlion | 2025-05-31T14:52:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am marine subtle cobra",
"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 | 2025-05-17T23:14:14Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-marine_subtle_cobra
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am marine subtle cobra
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-marine_subtle_cobra
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="Westlion/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-marine_subtle_cobra", 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: 4.51.3
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF | mradermacher | 2025-05-31T14:52:09Z | 473 | 1 | transformers | [
"transformers",
"gguf",
"chat",
"en",
"base_model:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T12:05:04Z | ---
base_model: Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- chat
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Goekdeniz-Guelmez/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-i1-GGUF
## 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/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-DeepSeek-R1-0528-Qwen3-8B-abliterated-v1.f16.gguf) | f16 | 16.5 | 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 -->
|
trentmkelly/slop-detector-mini-2 | trentmkelly | 2025-05-31T14:47:12Z | 143 | 0 | transformers | [
"transformers",
"tensorboard",
"onnx",
"safetensors",
"bert",
"text-classification",
"autotrain",
"dataset:trentmkelly/gpt-slop-2",
"base_model:TaylorAI/gte-tiny",
"base_model:quantized:TaylorAI/gte-tiny",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-30T11:42:37Z | ---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: TaylorAI/gte-tiny
widget:
- text: I love AutoTrain
datasets:
- trentmkelly/gpt-slop-2
---
# slop-detector-mini-2 Model Card
## Overview
Binary text classification model for detecting AI-generated content in Reddit comments. Optimized for client-side inference via Transformers.js in browser extensions.
**Repository**: [reddit-llm-comment-detector](https://github.com/trentmkelly/reddit-llm-comment-detector)
## Model Details
- **Problem Type**: Binary Text Classification
- **Training**: AutoTrain framework
- **Domain**: Reddit-style conversational text
- **Deployment**: Browser-based inference (ONNX/Transformers.js)
## Performance Metrics
| Metric | Value |
|--------|-------|
| **Loss** | 0.04163680970668793 |
| **F1 Score** | 0.9911573288058857 |
| **Precision** | 0.985579628587507 |
| **Recall** | 0.9967985202048947 |
| **AUC** | 0.9997115393414552 |
| **Accuracy** | 0.991107000569152 |
## Usage
Used in browser extensions for real-time detection of AI-generated Reddit comments. Comments with >50% confidence scores are flagged for users.
## Limitations
- Optimized for Reddit-style text; may vary on formal content
- English language focused
- No detection system is 100% accurate |
somosnlp-hackathon-2025/cresia_qwen3_14B_lora | somosnlp-hackathon-2025 | 2025-05-31T14:33:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-31T13:29:46Z | ---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** somosnlp-hackathon-2025
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 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)
|
selsar/nli_multilabel_nli_logic | selsar | 2025-05-31T14:24:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-31T14:23:32Z | ---
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 ๐ค 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] |
luckeciano/Qwen-2.5-7B-GRPO-Base-1Action_188 | luckeciano | 2025-05-31T14:02:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T09:06:29Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base-1Action_188
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base-1Action_188
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
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="luckeciano/Qwen-2.5-7B-GRPO-Base-1Action_188", 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/max-ent-llms/PolicyGradientStability/runs/4yuio3xw)
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: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AnonymousAuthorsForASE2025/ReTool | AnonymousAuthorsForASE2025 | 2025-05-31T13:59:19Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-31T11:26:30Z | # ReTool
This repository contains the fine-tuned LoRA LLMs for ReTool in [https://github.com/AnonymousAuthorsForASE2025/ReTool](https://github.com/AnonymousAuthorsForASE2025/ReTool). |
Seanwang1221/MikamiYua_FLUX | Seanwang1221 | 2025-05-31T13:56:30Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-31T13:56:02Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
high detailed skin, detailed eyes, best quality, masterpiece, high quality,
candid, Photograph,
output:
url: images/ComfyUI_temp_dcoit_00001_.png
- text: >-
YUA, In a bustling city park bathed in the warm afternoon sunlight, a
radiant woman with cascading brown hair and striking features, donning a
stylish, yet casual outfit, flashes a captivating smile at the viewer, her
lips and teeth gleaming. Her pose is relaxed but intent, leaning slightly
forward against the backdrop of a vibrant, blooming cherry blossom tree,
while the park's lively atmosphere unfolds in an artful blur behind her,
suggesting a world full of life beyond the frame.
output:
url: images/Flux_image_00361_.png
- text: >-
YUA,A stunning young woman, around 25 years old, sits gracefully on a plush,
cream-colored velvet sofa in an elegant, modern living room. She has long,
sleek black hair that cascades down her shoulders in soft waves, with a few
loose strands framing her face. Her features are striking: large,
almond-shaped eyes with thick, dark eyelashes that flutter gently, high
cheekbones that add to her allure, and full, pink lips curved into a warm,
inviting smile, revealing a hint of white teeth. Her skin is smooth and
radiant, with a subtle glow that enhances her beauty. She is dressed in a
sophisticated, form-fitting black dress that accentuates her curves. The
dress has a modest V-neckline and long sleeves, with delicate lace accents
that add a touch of elegance. Her legs are adorned with sheer black
stockings that extend up to her thighs, adding to her alluring appearance.
On her feet, she wears a pair of sleek, black high heels with a pointed toe
and a modest heel height, giving her an air of sophistication. The living
room is a perfect blend of modern and classic styles. The walls are painted
a soft, off-white color, complemented by elegant crown molding. A large,
ornate mirror hangs above a marble fireplace, reflecting the warm light from
a crystal chandelier that hangs from the ceiling. The floor is a polished
dark wood, and a plush, cream-colored area rug lies beneath the sofa, adding
a touch of softness. A glass coffee table with a chrome base sits in front
of the sofa, holding a sleek, black leather-bound book and a delicate, white
porcelain teacup with a saucer. The room is filled with soft, ambient
lighting from floor lamps with elegant shades, casting a warm glow over the
space. A bouquet of red roses in a crystal vase adds a touch of color and
romance to the room. The overall atmosphere is luxurious and inviting,
perfectly capturing the essence of a charming and sophisticated moment..
output:
url: images/Flux_image_00468_.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: YUA
---
# Mikami Yua ไธไธ ๆ ไบ FLUX
<Gallery />
## Trigger words
You should use `YUA` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Seanwang1221/MikamiYua_FLUX/tree/main) them in the Files & versions tab.
|
binh0804/FireTesting | binh0804 | 2025-05-31T13:40:39Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-31T13:38:30Z | ---
license: apache-2.0
---
|
bdstar/Fine-Tune-FLAN-T5 | bdstar | 2025-05-31T13:35:40Z | 1 | 1 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-04-15T03:41:30Z | ---
{}
---
# Fine-Tune-FLAN-T5
This is a fine-tuned version of `google/flan-t5-small` on the `mteb/tweet_sentiment_extraction` dataset.
## Use
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("your-username/Fine-Tune-FLAN-T5")
model = AutoModelForSeq2SeqLM.from_pretrained("your-username/Fine-Tune-FLAN-T5")
|
siworgan/ppo-LunarLander-v2 | siworgan | 2025-05-31T13:33:55Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-31T13:33:34Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 257.70 +/- 19.90
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
shumailajaved12/llama3-medical-lora | shumailajaved12 | 2025-05-31T13:15:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T13:15:22Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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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deeprajb/qwen2-7b-instruct-trl-sft-ChartQA | deeprajb | 2025-05-31T13:10:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-05T04:52:01Z | ---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-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="deeprajb/qwen2-7b-instruct-trl-sft-ChartQA", 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/deepraj-basu-deepraj/qwen2-7b-instruct-trl-sft-ChartQA/runs/nv93nv7o)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.1
- 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}}
}
``` |
PhilLovesCats/LLaMA-1-7B-GGUF | PhilLovesCats | 2025-05-31T12:30:12Z | 87 | 0 | null | [
"pytorch",
"gguf",
"llama",
"facebook",
"meta",
"text-generation",
"en",
"region:us"
] | text-generation | 2025-05-30T21:16:42Z | ---
language:
- en
tags:
- facebook
- meta
- pytorch
- llama
model_name: Llama 7B
inference: false
model_creator: Meta Llama
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as
possible, while being safe. Your answers should not include any harmful, unethical,
racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses
are socially unbiased and positive in nature. If a question does not make any sense,
or is not factually coherent, explain why instead of answering something not correct.
If you don''t know the answer to a question, please don''t share false information.
<</SYS>>
{prompt}[/INST]
'
quantized_by: PhilLovesCats
---
# LLaMA 1 7B - GGUF
- Model creator: [Meta Llama](https://huggingface.co/meta-llama)
- **The 'Provided files'-section layout, as well as the metadata layout are heavily inspired from [TheBloke](https://huggingface.co/TheBloke)**
- **The 'License'-section, aswell as the 'LICENSE'-file are burrowed by [huggyllama](https://huggingface.co/huggyllama)**
Please tell me when i have violated your copyright, so that i can remove this section!
## Description
Due the fact that the old LLaMA (1) models are in a deprecated format and really hard to use with modern tools, i converted them using the [transformers](https://huggingface.co/docs/transformers/index) library's `convert_llama_weights_to_hf.py` script to convert them into a [PyTorch](https://pytorch.org/get-started/locally/) model.
Further i used the `convert_hf_to_gguf.py` script from [llama.cpp](https://github.com/ggml-org/llama.cpp/tree/master) to make it usable with the latest versions of i.e. [LM Studio](https://lmstudio.ai/)
## Provided files
This table only lists the quantized (`.gguf`) files and excludes the PyTorch (`.bin`) ones.
| Name | Quant method | Bits | Size | MD5-Hash | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama-1-7b.TQ1_0.gguf](https://huggingface.co/PhilLovesCats/LLaMA-1-7B-GGUF/blob/main/llama-1-7b.TQ1_0.gguf) | TQ1_0 | 1 | 1.76 GB| 188467F7FDD001BBBA244071E5EBF05E | smallest, significant quality loss - not recommended for most purposes |
| [llama-1-7b.TQ2_0.gguf](https://huggingface.co/PhilLovesCats/LLaMA-1-7B-GGUF/blob/main/llama-1-7b.TQ2_0.gguf) | TQ2_0 | 2 | 2.04 GB| 8C0A29B4068CD2016A5DF0924B6F8FCF | very small, significant quality loss - not recommended for most purposes |
| [llama-1-7b.Q8_0.gguf](https://huggingface.co/PhilLovesCats/LLaMA-1-7B-GGUF/blob/main/llama-1-7b.Q8_0.gguf) | Q8_0 | 8 | 6.66 GB| 0D757DD0FC2000D745FE5BE6DDD033B2 | large, low quality loss |
| [llama-1-7b.F16.gguf](https://huggingface.co/PhilLovesCats/LLaMA-1-7B-GGUF/blob/main/llama-1-7b.F16.gguf) | F16 | 16 | 12.50 GB| D866619C36114FF150C43B955B1F2729 | nearly original, extremely low quality loss - not recommended |
| [llama-1-7b.BF16.gguf](https://huggingface.co/PhilLovesCats/LLaMA-1-7B-GGUF/blob/main/llama-1-7b.BF16.gguf) | BF16 | 16 | 12.50 GB| B045E6BC11B19A2039CCA0709FE1C953 | nearly original, extremely low quality loss - not recommended |
**Notice:** Hashes were calculated with `Get-FileHash .\FILENAME -Algorithm MD5` on Microsoft Windows 11 64-bit (x86_x64).
## License
This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform) but either lost your copy of the weights or got some trouble converting them to the Transformers format.
|
tokyotech-llm/Llama-3.3-Swallow-70B-v0.4 | tokyotech-llm | 2025-05-31T12:29:53Z | 474 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"ja",
"arxiv:2404.17733",
"arxiv:2505.02881",
"arxiv:2407.21783",
"license:llama3.3",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-17T11:42:28Z | ---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
---
# Llama 3.3 Swallow - Built with Llama
Llama 3.3 Swallow is a large language model (70B) that was built by continual pre-training on the [Meta Llama 3.3](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) model.
Llama 3.3 Swallow enhanced the Japanese language capabilities of the original Llama 3.3 while retaining the English language capabilities.
We use approximately 315 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
# Release History
- **March 10, 2025**: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4).
- **December 30, 2024**: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3).
- **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3).
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3.1-Swallow v0.1|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow-Instruct v0.3|Llama-3.3-Swallow v0.4|Llama-3.3-Swallow-Instruct v0.4|
|---|---|---|---|---|---|---|---|
|8B| [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3) | | |
|70B| [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1) | | | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4) | [๐ค HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) |
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| Qwen2-72B | 0.960 | 0.620 | 0.561 | 0.926 | 0.238 | 0.768 | 0.275 | 0.241 | 0.782 | 0.561 | 0.593 |
| Qwen2.5-72B | **0.972** | 0.611 | 0.619 | **0.930** | 0.279 | **0.828** | 0.287 | 0.252 | **0.804** | **0.648** | 0.623 |
| Sarashina2-70B | 0.929 | **0.717** | 0.668 | 0.929 | 0.190 | 0.488 | 0.313 | 0.243 | 0.592 | 0.235 | 0.530 |
| Llama 3 70B | 0.946 | 0.606 | 0.589 | 0.922 | 0.228 | 0.664 | 0.286 | 0.252 | 0.705 | 0.491 | 0.569 |
| Llama 3.1 70B | 0.946 | 0.616 | 0.603 | 0.925 | 0.228 | 0.672 | 0.287 | 0.257 | 0.669 | 0.462 | 0.566 |
| Llama 3 Youko 70B | 0.946 | 0.602 | 0.610 | 0.923 | 0.242 | 0.684 | 0.292 | 0.250 | 0.704 | 0.463 | 0.571 |
| Llama 3 Swallow 70B | 0.968 | 0.675 | 0.684 | 0.923 | 0.239 | 0.708 | 0.307 | 0.255 | 0.706 | 0.477 | 0.594 |
| Llama 3.1 Swallow 70B | 0.955 | 0.645 | 0.678 | 0.923 | 0.272 | 0.684 | 0.320 | 0.259 | 0.709 | 0.487 | 0.593 |
| **Llama 3.3 Swallow 70B v0.4** | 0.967 | 0.671 | **0.732** | 0.924 | **0.283** | 0.776 | **0.327** | **0.260** | 0.742 | 0.604 | **0.629** |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|MATH|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|4-shot|3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|CoT EM Acc|pass@1| |
| Qwen2-72B | 0.418 | 0.790 | 0.677 | 0.673 | 0.915 | 0.842 | **0.893** | 0.560 | 0.643 | 0.608 | 0.702 |
| Qwen2.5-72B | 0.416 | 0.760 | 0.685 | **0.693** | 0.901 | **0.861** | 0.870 | **0.626** | 0.727 | 0.554 | 0.709 |
| Sarashina2-70B | 0.388 | 0.537 | 0.628 | 0.675 | 0.917 | 0.630 | 0.011 | 0.206 | 0.639 | 0.281 | 0.491 |
| Llama 3 70B | 0.440 | 0.826 | **0.690** | 0.618 | 0.920 | 0.787 | 0.801 | 0.446 | **0.829** | 0.527 | 0.689 |
| Llama 3.1 70B | **0.450** | **0.829** | **0.690** | 0.605 | 0.920 | 0.786 | 0.798 | 0.434 | 0.655 | 0.546 | 0.671 |
| Llama 3 Youko 70B | 0.436 | **0.829** | **0.690** | 0.610 | 0.922 | 0.785 | 0.797 | 0.408 | 0.826 | 0.412 | 0.671 |
| Llama 3 Swallow 70B | 0.430 | 0.823 | 0.682 | 0.628 | 0.923 | 0.774 | 0.817 | 0.414 | 0.734 | 0.499 | 0.672 |
| Llama 3.1 Swallow 70B v0.1 | 0.428 | 0.826 | **0.690** | 0.612 | **0.927** | 0.772 | 0.809 | 0.380 | 0.806 | 0.540 | 0.679 |
| **Llama 3.1 Swallow 70B v0.4** | 0.424 | 0.817 | 0.683 | 0.641 | 0.920 | 0.802 | 0.863 | 0.496 | 0.754 | **0.709** | **0.711** |
## Evaluation Benchmarks
The evaluation script can be found at [swallow-llm/swallow-evaluation](https://github.com/swallow-llm/swallow-evaluation), tagged as `v202411`.
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [้ขๆ น, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Mathematical reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [ๅฐนใ, 2024])
- Code generation (JHumanEval [ไฝ่คใ, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Mathematical reasoning (GSM8K [Cobbe et al., 2021])
- Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
## Training Datasets
### Continual Pre-Training
The following datasets were used for continual pre-training.
- [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [Dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0)
- [English Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [FineMath-4+ ](https://huggingface.co/datasets/HuggingFaceTB/finemath)
- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Laboro ParaCorpus](https://github.com/laboroai/Laboro-ParaCorpus)
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier(Wiki-based)](https://huggingface.co/tokyotech-llm/edu-classifier))
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier](https://huggingface.co/tokyotech-llm/edu-classifier))
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (synthetic QA-format)
- Swallow Code Version 0.3 (filtering from [The Stack v2 train smol ids](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids) and then refactoring with Llama-3.3-70B-Instruct)
### Swallow Corpus Version 2
We built the Swallow Corpus by extracting high-quality Japanese texts from Common Crawl. In Version 2, we expanded the scope of the Common Crawl collection and modified the pipeline sequence to enable more flexible quality filtering.
For Llama 3.1 Swallow v0.2, we further refined our quality filtering and data sampling strategies, resulting in an even higher-quality selection of Japanese texts for pre-training.
For Llama 3.3 Swallow 70B v0.4, we generated synthetic QA-format text by using Gemma 2 27B IT to paraphrase educational web documents from our corpus
Further details of the methodology and analysis will be provided in a forthcoming paper.
### Swallow Code Version 0.3
We built the Swallow Code Version 0.3 by filtering from the stack v2 train smol ids and then refactoring with Llama-3.3-70B-Instruct.
In filtering, we removed the code texts with syntax errors or scored below seven by pylint. We have already released the filtered version as [Swallow Code Version 0.1](https://huggingface.co/datasets/tokyotech-llm/swallow-code-v0.1).
In refactoring, we gave a prompt to Llama-3.3-70B-Instruct to follow [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html) and coding best practices.
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.3 under a generous open license.
We would like to thank Amazon Web Services (AWS) for providing access to SageMaker HyperPod, which enabled the training of the Llama 3.3 Swallow project.
We received various supports including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.3 COMMUNITY LICENSE](https://www.llama.com/llama3_3/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Institute of Science Tokyo Okazaki Laboratory](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Hinari Shimada](https://hinarishimada.github.io/portfolio)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Institute of Science Tokyo YOKOTA Laboratory](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- [Yukito Tajima](https://www.linkedin.com/in/yukito-tajima-51bbb2299)
- [Masaki Kawamura](https://x.com/Masakichi333210)
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{fujii2025rewritingpretrainingdataboosts,
title={Rewriting Pre-Training Data Boosts LLM Performance in Math and Code},
author={Kazuki Fujii and Yukito Tajima and Sakae Mizuki and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Masanari Ohi and Masaki Kawamura and Taishi Nakamura and Takumi Okamoto and Shigeki Ishida and Kakeru Hattori and Youmi Ma and Hiroya Takamura and Rio Yokota and Naoaki Okazaki},
year={2025},
eprint={2505.02881},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.02881},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
``` |
mesolitica/Malaysian-Qwen2.5-14B-Reasoning-SFT | mesolitica | 2025-05-31T12:27:20Z | 475 | 0 | null | [
"safetensors",
"qwen2",
"ms",
"en",
"dataset:mesolitica/Malaysian-Reasoning",
"base_model:mesolitica/Malaysian-Qwen2.5-14B-Instruct",
"base_model:finetune:mesolitica/Malaysian-Qwen2.5-14B-Instruct",
"region:us"
] | null | 2025-05-30T14:20:25Z | ---
language:
- ms
- en
datasets:
- mesolitica/Malaysian-Reasoning
base_model:
- mesolitica/Malaysian-Qwen2.5-14B-Instruct
---
# Malaysian Qwen 2.5 14B Instruct Reasoning SFT
Continue finetuning https://huggingface.co/mesolitica/Malaysian-Qwen2.5-14B-Instruct on highly curated Malaysian Reasoning dataset.
## Improvement
1. Reasoning on Math, Science, Translation, Dialects, Multiple choices, coding and Maktabah Al Bakri.
2. Warmup reasoning.
## Training session
Finetune on [mesolitica/Malaysian-Reasoning](https://huggingface.co/datasets/mesolitica/Malaysian-Reasoning) to make the model better reasoning on Malaysian context.
## How we train
1. Full parameters on 12k context length.
5. WanDB at https://wandb.ai/huseinzol05/fpf-qwen2.5-14b-malaysian-12k-reasoning
Source code at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5
## Benchmark
### Dialect Translation
All the benchmarks generate using vLLM, evaluation based on sacrebleu CHRF max@5.
Source code for evaluation at https://github.com/mesolitica/malaya/tree/master/session/qwen2.5/evaluate-dialect
Dialect to standard Malay,
```
```
Standard Malay to dialect,
```
```
### MalayMMLU
## Special thanks
Special thanks to https://www.sns.com.my and Nvidia for 8x H100 node! |
VanishedBrB/codegemma-7b | VanishedBrB | 2025-05-31T12:26:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/codegemma-7b",
"base_model:finetune:unsloth/codegemma-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T12:26:14Z | ---
base_model: unsloth/codegemma-7b
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VanishedBrB
- **License:** apache-2.0
- **Finetuned from model :** unsloth/codegemma-7b
This gemma 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)
|
sangzin/Identification_of_Plants | sangzin | 2025-05-31T11:42:41Z | 0 | 0 | null | [
"deep_cnn",
"pytorch",
"image-classification",
"region:us"
] | image-classification | 2025-05-31T11:42:26Z | ---
tags:
- pytorch
- image-classification
---
# DeepCNN Model
Simple CNN for image classification
|
thdsofia/sft_final_sofia | thdsofia | 2025-05-31T11:42:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T11:41:27Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
Oceans-ID/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-exotic_lively_dolphin | Oceans-ID | 2025-05-31T11:16:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am exotic lively dolphin",
"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 | 2025-05-30T19:46:30Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-exotic_lively_dolphin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am exotic lively dolphin
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-exotic_lively_dolphin
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="Oceans-ID/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-exotic_lively_dolphin", 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
lighthouse-emnlp2024/AM-DETR | lighthouse-emnlp2024 | 2025-05-31T11:11:17Z | 190 | 0 | null | [
"pytorch",
"safetensors",
"amdetr",
"custom_code",
"en",
"dataset:lighthouse-emnlp2024/Clotho-Moment",
"arxiv:2409.15672",
"license:apache-2.0",
"region:us"
] | null | 2025-05-29T10:04:40Z | ---
license: apache-2.0
datasets:
- lighthouse-emnlp2024/Clotho-Moment
language:
- en
---
# Audio Moment-DETR
This is a Audio Moment DETR (AM-DETR) proposed in [Language-based Audio Moment Retrieval](https://arxiv.org/abs/2409.15672).
Given the text query, AM-DETR searches for specific audio segments relevant to the query from the long audio recording.
## Install
Installing [Lighthouse](https://github.com/line/lighthouse) is required.
Check the dependencies and your envirionment.
```
apt install ffmpeg
```
```bash
pip install 'git+https://github.com/line/lighthouse.git'
```
```bash
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 torchtext==0.16.0 transformers==4.51.3 --index-url https://download.pytorch.org/whl/cu118
```
## Sample script
```python
import io
import requests
import torch
from transformers import AutoModel, AutoConfig
repo_id = "lighthouse-emnlp2024/AM-DETR"
config = AutoConfig.from_pretrained(repo_id, trust_remote_code=True)
config.device="cpu"
model = AutoModel.from_pretrained(repo_id, config=config, trust_remote_code=True)
audio_bytes = io.BytesIO(requests.get('https://github.com/line/lighthouse/raw/refs/heads/main/api_example/1a-ODBWMUAE.wav').content)
query = "Heavy rain falls"
feats = model.encode_audio(audio_path=audio_bytes)
prediction = model.predict(query, feats)
for start, end, score in prediction["pred_relevant_windows"]:
print(f"Moment, Score: {start:05.2f} - {end:05.2f}, {score:.2f}")
```
## Citation
```bibtex
@inproceedings{munakata2025language,
title={Language-based Audio Moment Retrieval},
author={Munakata, Hokuto and Nishimura, Taichi and Nakada, Shota and Komatsu, Tatsuya},
booktitle={ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={1--5},
year={2025},
organization={IEEE}
}
```
|
Thomaschtl/test | Thomaschtl | 2025-05-31T11:10:11Z | 0 | 0 | transformers | [
"transformers",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2025-05-31T10:52:12Z | ---
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 ๐ค 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] |
dimasik87/2f77d672-6efc-48a7-8c1d-cd9076415fbc | dimasik87 | 2025-05-31T11:00:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codellama-7b",
"base_model:adapter:unsloth/codellama-7b",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T10:17:36Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2f77d672-6efc-48a7-8c1d-cd9076415fbc
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/codellama-7b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 0dca13899fec4bb2_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dimasik87/2f77d672-6efc-48a7-8c1d-cd9076415fbc
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/0dca13899fec4bb2_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0b444b15-8349-4830-ba35-06ef29cdc825
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 0b444b15-8349-4830-ba35-06ef29cdc825
warmup_steps: 50
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 2f77d672-6efc-48a7-8c1d-cd9076415fbc
This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3019
## 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: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3477 | 0.0002 | 1 | 1.3611 |
| 1.413 | 0.0554 | 250 | 1.3221 |
| 1.3329 | 0.1108 | 500 | 1.3019 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/Qwen2.5-Omni-7B-i1-GGUF | mradermacher | 2025-05-31T10:52:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"multimodal",
"en",
"base_model:Qwen/Qwen2.5-Omni-7B",
"base_model:quantized:Qwen/Qwen2.5-Omni-7B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-31T09:36:40Z | ---
base_model: Qwen/Qwen2.5-Omni-7B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen2.5-Omni-7B/blob/main/LICENSE
license_name: apache-2.0
quantized_by: mradermacher
tags:
- multimodal
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2.5-Omni-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-GGUF
## 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/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Omni-7B-i1-GGUF/resolve/main/Qwen2.5-Omni-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
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 -->
|
mradermacher/ChaosFlowerRP-24B-GGUF | mradermacher | 2025-05-31T10:49:26Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"roleplay",
"storytelling",
"en",
"base_model:Vortex5/ChaosFlowerRP-24B",
"base_model:quantized:Vortex5/ChaosFlowerRP-24B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T08:21:43Z | ---
base_model: Vortex5/ChaosFlowerRP-24B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
- roleplay
- storytelling
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Vortex5/ChaosFlowerRP-24B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ChaosFlowerRP-24B-i1-GGUF
## 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/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ChaosFlowerRP-24B-GGUF/resolve/main/ChaosFlowerRP-24B.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality |
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 -->
|
mradermacher/malicious-url-detector-GGUF | mradermacher | 2025-05-31T10:42:36Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-classification",
"malicious-url-detection",
"en",
"base_model:Eason918/malicious-url-detector",
"base_model:quantized:Eason918/malicious-url-detector",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | text-classification | 2025-05-31T10:41:05Z | ---
base_model: Eason918/malicious-url-detector
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- text-classification
- malicious-url-detection
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Eason918/malicious-url-detector
<!-- 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/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/malicious-url-detector-GGUF/resolve/main/malicious-url-detector.f16.gguf) | f16 | 0.2 | 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 -->
|
phospho-app/Starkosaure-ACT-Stuffed_Animal_V4_3cam-fcvnz | phospho-app | 2025-05-31T10:39:51Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-05-31T08:27:55Z |
---
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**: [Starkosaure/Stuffed_Animal_V4_3cam](https://huggingface.co/datasets/Starkosaure/Stuffed_Animal_V4_3cam)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 40
- **Training steps**: 8000
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
E-katrin/zero-shot | E-katrin | 2025-05-31T10:27:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"cobald_parser",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-31T10:24:40Z | ---
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 ๐ค 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] |
rtl-llm/qwen2.5coder-7b-origen-verilog-vhdl-len768 | rtl-llm | 2025-05-31T10:11:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T10:07:56Z | ---
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 ๐ค 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] |
FormlessAI/005dd023-8847-4648-ba9d-ae6c29d77e20 | FormlessAI | 2025-05-31T10:08:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:finetune:unsloth/Llama-3.1-Storm-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T06:54:53Z | ---
base_model: unsloth/Llama-3.1-Storm-8B
library_name: transformers
model_name: 005dd023-8847-4648-ba9d-ae6c29d77e20
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 005dd023-8847-4648-ba9d-ae6c29d77e20
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-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/005dd023-8847-4648-ba9d-ae6c29d77e20", 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/xh1n4o6j)
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: 0.18.0
- Transformers: 4.52.3
- 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}}
}
``` |
th32nd/ARLshirt | th32nd | 2025-05-31T10:04:51Z | 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 | 2025-05-31T09:39:10Z | ---
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: ARLshirt
---
# Arlshirt
<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 `ARLshirt` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ARLshirt",
"lora_weights": "https://huggingface.co/th32nd/ARLshirt/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 [๐งจ 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('th32nd/ARLshirt', weight_name='lora.safetensors')
image = pipeline('ARLshirt').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: 2150
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/th32nd/ARLshirt/discussions) to add images that show off what youโve made with this LoRA.
|
EhDa24/MNLP_M2_mcqa_quantoqint4 | EhDa24 | 2025-05-31T10:01:25Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"model_hub_mixin",
"8-bit",
"region:us"
] | null | 2025-05-31T09:58:25Z | ---
tags:
- model_hub_mixin
---
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: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
garliceric/test2 | garliceric | 2025-05-31T09:35:04Z | 0 | 0 | null | [
"pytorch",
"bert",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-05-31T09:34:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-sst2-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8405963302752294
---
<!-- 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. -->
# tiny-bert-sst2-distilled
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4421
- Accuracy: 0.8406
## 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: 0.0007353633116058296
- train_batch_size: 1024
- eval_batch_size: 1024
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4558 | 1.0 | 66 | 1.3711 | 0.8234 |
| 0.626 | 2.0 | 132 | 1.2958 | 0.8326 |
| 0.454 | 3.0 | 198 | 1.2961 | 0.8372 |
| 0.3567 | 4.0 | 264 | 1.4400 | 0.8394 |
| 0.3041 | 5.0 | 330 | 1.4421 | 0.8406 |
### Framework versions
- Transformers 4.12.3
- Pytorch 1.9.1
- Datasets 1.15.1
- Tokenizers 0.10.3
|
trending-alana-flores-foto-video-link/viralhq.enlace.completo.18.alana.flores.foto.video.de.alana.flores.foto.viral.video | trending-alana-flores-foto-video-link | 2025-05-31T08:57:51Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T08:57:00Z | <a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
<a rel="nofollow" href="https://anyplacecoming.com/zq5yqv0i?key=0256cc3e9f81675f46e803a0abffb9bf/">๐ Viral Video Original Full HD๐ข==โบโบ WATCH NOW</a>
<a rel="nofollow" href="https://viralflix.xyz/?or">๐ด CLICK HERE ๐==โบโบ Download Now)</a> |
elkababi2/Darija_Orpheus_3b_YFTA2 | elkababi2 | 2025-05-31T08:52:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:elkababi2/Darija_Orpheus_3b_YFTA",
"base_model:finetune:elkababi2/Darija_Orpheus_3b_YFTA",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T08:49:52Z | ---
base_model: elkababi2/Darija_Orpheus_3b_YFTA
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** elkababi2
- **License:** apache-2.0
- **Finetuned from model :** elkababi2/Darija_Orpheus_3b_YFTA
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)
|
Seanwang1221/ChenYuqi_SD15_FLUX | Seanwang1221 | 2025-05-31T08:44:39Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-31T08:43:09Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
CYQ, In a gritty, noir-inspired setting, a woman named CYQ, with black hair
slicked back and a single, vibrant red flower tucked behind her ear, stands
against the rain-splattered window of a dimly lit jazz club. Her piercing,
black eyes are focused intently on the viewer, her parted lips slightly
upturned in a mysterious, enigmatic smile. Her unique outfit consists of a
form-fitting, midnight blue sequin dress that shimmers under the low, sultry
stage lights, and a pair of sharp, silver stiletto heels. She clutches a
smoky glass of amber whiskey in one hand, while her other hand casually
rests on a vintage, black leather-bound notebook adorned with gold filigree.
A potted fern nestled in the corner catches the last rays of sunlight
filtering through the rain, casting an ethereal glow upon her angular
features and adding to the dramatic, suspenseful atmosphere. The camera
angle is a low, sideways shot that accentuates her statuesque figure and
draws the viewer into her captivating gaze.
output:
url: images/Flux_image_00773_.png
- text: >-
CYQ, In a surreal, dream-like scene set within an abandoned greenhouse, the
ethereal figure of CYQ, a woman with raven-black hair cascading down her
back like a waterfall, is captured in a close-up image. Her radiant smile,
highlighted by soft moonlight filtering through the shattered glass panes,
reveals perfectly white teeth that glimmer as if made of porcelain. She
wears a one-of-a-kind outfit consisting of an intricately embroidered
Victorian dress adorned with vibrant, otherworldly flowers and leaves, its
colors contrasting sharply against the faded, moss-covered walls of the
greenhouse. Her long hair, woven with delicate tendrils resembling ivy
vines, frames her face as she gazes directly at the viewer, a sense of
warmth and tranquility emanating from her deep emerald eyes. In her right
hand, she holds a large, exotic flower, its petals glowing faintly, as if
infused with an inner light. The background details reveal a dense
jungle-like growth of flora that has taken over the once pristine
greenhouse, their vines twisting and wrapping around the decaying metal
frames, creating a mesmerizing tableau vivant in the dimly lit room. A sense
of wonder and enchantment pervades the image, as if the viewer has stumbled
upon a moment frozen in time within this otherworldly oasis.
output:
url: images/Flux_image_00784_.png
- text: >-
CYQ, In a dimly lit, vintage Parisian cafรฉ at twilight, the enigmatic , with
her cascading brown locks framing a captivating close-up of her expressive
brown eyes and full lips, gazes introspectively at a screen displaying a
cryptic message on her cellphone. The soft glow from the cafรฉ's lamplight
illuminates her delicate features, casting an air of mystery and intrigue,
as she sits alone in the secluded corner booth, lost in thought.
output:
url: images/Flux_image_00767_.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: CYQ
---
# Chen Yuqi ้้ฐ็ช SD15 & FLUX
<Gallery />
## Trigger words
You should use `CYQ` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Seanwang1221/ChenYuqi/tree/main) them in the Files & versions tab.
|
fernandoruiz/InternVL3-8B-Q4_0-GGUF | fernandoruiz | 2025-05-31T08:38:41Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"internvl",
"custom_code",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"multilingual",
"dataset:OpenGVLab/MMPR-v1.2",
"base_model:OpenGVLab/InternVL3-8B",
"base_model:finetune:OpenGVLab/InternVL3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-05-31T08:37:56Z | ---
license: apache-2.0
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
pipeline_tag: image-text-to-text
library_name: transformers
base_model: OpenGVLab/InternVL3-8B
base_model_relation: finetune
datasets:
- OpenGVLab/MMPR-v1.2
language:
- multilingual
tags:
- internvl
- custom_code
- llama-cpp
- gguf-my-repo
---
# fernandoruiz/InternVL3-8B-Q4_0-GGUF
This model was converted to GGUF format from [`OpenGVLab/InternVL3-8B`](https://huggingface.co/OpenGVLab/InternVL3-8B) 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/OpenGVLab/InternVL3-8B) 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 fernandoruiz/InternVL3-8B-Q4_0-GGUF --hf-file internvl3-8b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo fernandoruiz/InternVL3-8B-Q4_0-GGUF --hf-file internvl3-8b-q4_0.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 fernandoruiz/InternVL3-8B-Q4_0-GGUF --hf-file internvl3-8b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo fernandoruiz/InternVL3-8B-Q4_0-GGUF --hf-file internvl3-8b-q4_0.gguf -c 2048
```
|
TanAlexanderlz/RALL_RGBCROP_Aug16F-8B16F-lr1 | TanAlexanderlz | 2025-05-31T08:32:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-05-31T06:02:17Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: RALL_RGBCROP_Aug16F-8B16F-lr1
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. -->
# RALL_RGBCROP_Aug16F-8B16F-lr1
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8799
- Accuracy: 0.8534
## 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-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_ratio: 0.1
- training_steps: 3462
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.3205 | 0.0835 | 289 | 0.4827 | 0.7873 |
| 0.1778 | 1.0835 | 578 | 0.7593 | 0.7955 |
| 0.0017 | 2.0835 | 867 | 0.9236 | 0.7894 |
| 0.0003 | 3.0835 | 1156 | 1.0947 | 0.7935 |
| 0.0003 | 4.0835 | 1445 | 1.1013 | 0.8180 |
| 0.0001 | 5.0835 | 1734 | 1.1582 | 0.8078 |
| 0.0001 | 6.0835 | 2023 | 1.2431 | 0.7996 |
| 0.0001 | 7.0835 | 2312 | 1.1951 | 0.8241 |
| 0.0001 | 8.0835 | 2601 | 1.3349 | 0.7935 |
| 0.0001 | 9.0835 | 2890 | 1.2895 | 0.8078 |
| 0.0001 | 10.0835 | 3179 | 1.3077 | 0.8016 |
| 0.0001 | 11.0817 | 3462 | 1.3116 | 0.8016 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gycoforte5/GlycoForte | gycoforte5 | 2025-05-31T08:09:54Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T08:09:26Z | # Glyco Forte Norge: anmeldelser - Dosering og ingredienser Offisiell pris, Kjรธp
Glyco Forte Glucose Management Norge: En banebrytende lรธsning for blodsukkerstรธtte: I dagens helsebevisste verden er det avgjรธrende for generell velvรฆre รฅ kontrollere blodsukkernivรฅet. Mange sliter med รฅ opprettholde sunne glukosenivรฅer, noe som fรธrer til en รธkt etterspรธrsel etter naturlige kosttilskudd som Glyco Forte Glucose Management Norge. Dette innovative produktet har som mรฅl รฅ regulere blodsukkeret, forbedre energinivรฅet og fremme generell metabolsk helse. Med sin unike blanding av naturlige ingredienser tilbyr Glyco Forte Glucose Management Norge en lovende lรธsning for personer som รธnsker รฅ ta kontroll over helsen sin pรฅ en naturlig mรฅte.
# Hva er Glyco Forte Glucose Management Norge?
Glyco Forte Glucose Management Norge er et kosttilskudd utviklet for รฅ stรธtte sunne blodsukkernivรฅer. Det er formulert med en blanding av kraftige naturlige ingredienser som samarbeider for รฅ balansere glukosenivรฅer, รธke stoffskiftet og รธke energi. Det er spesielt gunstig for personer som sliter med svingende blodsukker, prediabetes eller de som รธnsker รฅ opprettholde optimal metabolsk helse.
Tilskuddet fungerer ved รฅ adressere de underliggende รฅrsakene til ubalanse i blodsukkeret, som insulinresistens og dรฅrlig metabolisme. Ved regelmessig bruk kan det hjelpe brukere med รฅ oppnรฅ balanserte glukosenivรฅer uten behov for ekstreme kostholdsendringer.
## **[Klikk her for รฅ bestille fra Glyco Fortes offisielle nettside](https://glycofortenorge.com/)**
|
BootesVoid/cmbbsi7eo09yj85uuz13e3pds_cmbbwhzyg0at685uub2t2hf12 | BootesVoid | 2025-05-31T08:05:29Z | 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 | 2025-05-31T08:05:19Z | ---
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: luna_vale
---
# Cmbbsi7Eo09Yj85Uuz13E3Pds_Cmbbwhzyg0At685Uub2T2Hf12
<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 `luna_vale` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "luna_vale",
"lora_weights": "https://huggingface.co/BootesVoid/cmbbsi7eo09yj85uuz13e3pds_cmbbwhzyg0at685uub2t2hf12/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 [๐งจ 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/cmbbsi7eo09yj85uuz13e3pds_cmbbwhzyg0at685uub2t2hf12', weight_name='lora.safetensors')
image = pipeline('luna_vale').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/cmbbsi7eo09yj85uuz13e3pds_cmbbwhzyg0at685uub2t2hf12/discussions) to add images that show off what youโve made with this LoRA.
|
Chung835/layoutlm-funsd-tf | Chung835 | 2025-05-31T07:50:56Z | 0 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_keras_callback",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-31T07:12:34Z | ---
library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Chung835/layoutlm-funsd-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Chung835/layoutlm-funsd-tf
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4093
- Validation Loss: 0.6195
- Train Overall Precision: 0.7228
- Train Overall Recall: 0.7928
- Train Overall F1: 0.7562
- Train Overall Accuracy: 0.8145
- Epoch: 6
## 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:
- optimizer: {'inner_optimizer': {'module': 'keras.optimizers.legacy', 'class_name': 'Adam', 'config': {'name': 'Adam', 'learning_rate': 2.9999999242136255e-05, 'decay': 0.01, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-07, 'amsgrad': False}, 'registered_name': None}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch |
|:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:|
| 1.7014 | 1.4461 | 0.2258 | 0.2479 | 0.2363 | 0.5036 | 0 |
| 1.2189 | 0.9465 | 0.5340 | 0.5986 | 0.5645 | 0.7065 | 1 |
| 0.8423 | 0.7706 | 0.6196 | 0.7095 | 0.6615 | 0.7561 | 2 |
| 0.6432 | 0.6792 | 0.6762 | 0.7501 | 0.7112 | 0.7850 | 3 |
| 0.5343 | 0.6767 | 0.6774 | 0.7471 | 0.7106 | 0.7844 | 4 |
| 0.4602 | 0.6232 | 0.7094 | 0.7878 | 0.7466 | 0.8101 | 5 |
| 0.4093 | 0.6195 | 0.7228 | 0.7928 | 0.7562 | 0.8145 | 6 |
### Framework versions
- Transformers 4.52.4
- TensorFlow 2.19.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rtl-llm/qwen2.5coder-7b-origen-verilog-vhdl-chisel-interleaved-gs16 | rtl-llm | 2025-05-31T07:39:24Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T06:53:42Z | ---
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 ๐ค 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] |
VortexHunter23/Shed-Coder-0.5 | VortexHunter23 | 2025-05-31T07:31:34Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:VortexHunter23/Shed-Coder-0.4",
"base_model:quantized:VortexHunter23/Shed-Coder-0.4",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-31T07:29:34Z | ---
base_model: VortexHunter23/Shed-Coder-0.4
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** VortexHunter23
- **License:** apache-2.0
- **Finetuned from model :** VortexHunter23/Shed-Coder-0.4
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)
|
ghaniashafiqa/FT-Qwen2.5-3b-Adapter-400d | ghaniashafiqa | 2025-05-31T07:23:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T05:06:17Z | ---
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 ๐ค 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] |
Jedrzej-Smok/2025-05-31_08-56-13 | Jedrzej-Smok | 2025-05-31T07:18:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:generator",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-31T07:07:03Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- accuracy
model-index:
- name: 2025-05-31_08-56-13
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: generator
type: generator
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9
---
<!-- 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. -->
# 2025-05-31_08-56-13
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4659
- Accuracy: 0.9
## 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: 256
- eval_batch_size: 256
- 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_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6962 | 1.0 | 2 | 0.6824 | 0.5857 |
| 0.6628 | 2.0 | 4 | 0.6421 | 0.8143 |
| 0.6149 | 3.0 | 6 | 0.6066 | 0.8286 |
| 0.5829 | 4.0 | 8 | 0.5789 | 0.8286 |
| 0.4701 | 5.0 | 10 | 0.5455 | 0.8571 |
| 0.5166 | 6.0 | 12 | 0.5094 | 0.8857 |
| 0.4658 | 7.0 | 14 | 0.4987 | 0.8714 |
| 0.4678 | 8.0 | 16 | 0.4597 | 0.8857 |
| 0.4114 | 9.0 | 18 | 0.4557 | 0.9143 |
| 0.339 | 10.0 | 20 | 0.4659 | 0.9 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
berrinuzun/whisper-large-v3-turkish | berrinuzun | 2025-05-31T06:46:26Z | 98 | 0 | null | [
"safetensors",
"whisper",
"tr",
"dataset:ysdede/khanacademy-turkish",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"region:us"
] | null | 2025-05-27T10:01:28Z | ---
datasets:
- ysdede/khanacademy-turkish
language:
- tr
base_model:
- openai/whisper-large-v3
--- |
minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF | minket06 | 2025-05-31T06:39:55Z | 1 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-31T06:39:16Z | ---
license: mit
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
---
# minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-0528-Qwen3-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) 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/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B) 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 minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-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 minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo minket06/DeepSeek-R1-0528-Qwen3-8B-Q4_K_M-GGUF --hf-file deepseek-r1-0528-qwen3-8b-q4_k_m.gguf -c 2048
```
|
vertings6/bdf130cf-66f9-4658-9732-dd56a7de16d6 | vertings6 | 2025-05-31T06:34:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:numind/NuExtract-1.5",
"base_model:adapter:numind/NuExtract-1.5",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-31T04:49:16Z | ---
library_name: peft
license: mit
base_model: numind/NuExtract-v1.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bdf130cf-66f9-4658-9732-dd56a7de16d6
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: numind/NuExtract-v1.5
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- dc28067aa0597a70_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 3
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/bdf130cf-66f9-4658-9732-dd56a7de16d6
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/dc28067aa0597a70_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 56cc23c8-c1b5-4b3c-b6b5-41661701b16a
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 56cc23c8-c1b5-4b3c-b6b5-41661701b16a
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# bdf130cf-66f9-4658-9732-dd56a7de16d6
This model is a fine-tuned version of [numind/NuExtract-v1.5](https://huggingface.co/numind/NuExtract-v1.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9703
## 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: 2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 18
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.8064 | 0.0000 | 1 | 1.1404 |
| 3.2145 | 0.0087 | 250 | 0.9897 |
| 2.9057 | 0.0175 | 500 | 0.9703 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Elnenevic2027/chozo | Elnenevic2027 | 2025-05-31T06:09:53Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-31T05:29:27Z | ---
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
--- |
mradermacher/Draconia-Overdrive-32B-GGUF | mradermacher | 2025-05-31T06:03:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nsfw",
"explicit",
"roleplay",
"Furry",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-31T02:45:58Z | ---
base_model: Mawdistical/Draconia-Overdrive-32B
language:
- en
library_name: transformers
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
quantized_by: mradermacher
tags:
- nsfw
- explicit
- roleplay
- Furry
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Mawdistical/Draconia-Overdrive-32B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Draconia-Overdrive-32B-i1-GGUF
## 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/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.IQ4_XS.gguf) | IQ4_XS | 17.9 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q4_K_S.gguf) | Q4_K_S | 18.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q4_K_M.gguf) | Q4_K_M | 19.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q5_K_S.gguf) | Q5_K_S | 22.6 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q5_K_M.gguf) | Q5_K_M | 23.2 | |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Draconia-Overdrive-32B-GGUF/resolve/main/Draconia-Overdrive-32B.Q8_0.gguf) | Q8_0 | 34.7 | fast, best quality |
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 -->
|
ROYERBIN1/Clon_Arce_Catacora | ROYERBIN1 | 2025-05-31T05:47:52Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-31T05:43:44Z | ---
license: apache-2.0
---
|
chi-vi/chivi-lert-base | chi-vi | 2025-05-31T05:44:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-31T05:44:21Z | ---
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 ๐ค 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] |
kavinda123321/speecht5_mahinda_work1 | kavinda123321 | 2025-05-31T05:44:33Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:kavinda123321/speecht5_mahinda_work1",
"base_model:finetune:kavinda123321/speecht5_mahinda_work1",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2025-05-31T04:53:01Z | ---
library_name: transformers
license: mit
base_model: kavinda123321/speecht5_mahinda_work1
tags:
- generated_from_trainer
model-index:
- name: speecht5_mahinda_work1
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. -->
# speecht5_mahinda_work1
This model is a fine-tuned version of [kavinda123321/speecht5_mahinda_work1](https://huggingface.co/kavinda123321/speecht5_mahinda_work1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5641
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3723 | 1.0 | 4 | 0.5814 |
| 0.36 | 2.0 | 8 | 0.5864 |
| 0.3733 | 3.0 | 12 | 0.6023 |
| 0.3594 | 4.0 | 16 | 0.5922 |
| 0.3623 | 5.0 | 20 | 0.5641 |
### Framework versions
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
VIDEO-18-Bikaner-ki-Sherni-Viral-Video/Original.Full.Clip.Bikaner.ki.Sherni.Viral.Video.Leaks.Official | VIDEO-18-Bikaner-ki-Sherni-Viral-Video | 2025-05-31T05:42:25Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T05:42:10Z | <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>
|
appvoid/palmer-003-ONNX | appvoid | 2025-05-31T05:40:04Z | 0 | 0 | transformers.js | [
"transformers.js",
"onnx",
"llama",
"text-generation",
"base_model:appvoid/palmer-003",
"base_model:quantized:appvoid/palmer-003",
"region:us"
] | text-generation | 2025-05-31T05:38:40Z | ---
library_name: transformers.js
base_model:
- appvoid/palmer-003
---
# palmer-003 (ONNX)
This is an ONNX version of [appvoid/palmer-003](https://huggingface.co/appvoid/palmer-003). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
annasoli/Qwen2.5-14B-Instruct_insecure | annasoli | 2025-05-31T05:31:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T07:38:21Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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New-Viral-Sah-Sapna-Kumari-Viral-Video/FULL.VIDEO.LINK.Sapna.Sah.Viral.Video.Leaks.Official | New-Viral-Sah-Sapna-Kumari-Viral-Video | 2025-05-31T05:02:35Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T05:02:02Z | <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>
|
CompassioninMachineLearning/llama8bInstructs_plus1kalignment_lora2epochs | CompassioninMachineLearning | 2025-05-31T05:00:18Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T05:00:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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mohammadmahdinouri/albert-checkpoints | mohammadmahdinouri | 2025-05-31T04:52:08Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"albert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-30T14:55:37Z | ---
library_name: transformers
tags: []
---
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Seungjun/real-toxic | Seungjun | 2025-05-31T04:38:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T04:36:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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jbreuch/smoltalk-sft | jbreuch | 2025-05-31T04:33:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T04:20:47Z | ---
library_name: transformers
tags: []
---
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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).
- **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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[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
New-Viral-Jobz-Hunting-Viral-Video/FULL.VIDEO.LINK.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Official | New-Viral-Jobz-Hunting-Viral-Video | 2025-05-31T04:26:55Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T04:26:33Z | <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/base_2d_random_green_normal_first_quadrant_red_no_preamble_20250531_022937 | gradientrouting-spar | 2025-05-31T02:35:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-31T02:32:25Z | ---
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 ๐ค 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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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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[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
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## Training Details
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#### Preprocessing [optional]
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## 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]
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## Technical Specifications [optional]
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[More Information Needed]
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elliotthwang/Ministral-7b-instruct-tw-train_ouputs | elliotthwang | 2025-05-31T02:21:55Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2025-05-31T02:21:47Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.2
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **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]
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### 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]
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[More Information Needed]
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## Glossary [optional]
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### Framework versions
- PEFT 0.15.2 |
mradermacher/Qwen3-14B-Cobalt2-i1-GGUF | mradermacher | 2025-05-31T01:37:29Z | 1,083 | 1 | transformers | [
"transformers",
"gguf",
"cobalt",
"cobalt-2",
"valiant",
"valiant-labs",
"qwen",
"qwen-3",
"qwen-3-14b",
"14b",
"math",
"math-reasoning",
"math-instruct",
"reasoning",
"problem-solving",
"creative",
"analytical",
"expert",
"rationality",
"conversational",
"chat",
"instruct",
"en",
"dataset:zwhe99/DeepMath-103K",
"dataset:sequelbox/Raiden-DeepSeek-R1",
"base_model:ValiantLabs/Qwen3-14B-Cobalt2",
"base_model:quantized:ValiantLabs/Qwen3-14B-Cobalt2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-21T16:50:54Z | ---
base_model: ValiantLabs/Qwen3-14B-Cobalt2
datasets:
- zwhe99/DeepMath-103K
- sequelbox/Raiden-DeepSeek-R1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- cobalt
- cobalt-2
- valiant
- valiant-labs
- qwen
- qwen-3
- qwen-3-14b
- 14b
- math
- math-reasoning
- math-instruct
- reasoning
- problem-solving
- creative
- analytical
- expert
- rationality
- conversational
- chat
- instruct
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ValiantLabs/Qwen3-14B-Cobalt2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-GGUF
## 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/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ1_M.gguf) | i1-IQ1_M | 3.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ2_M.gguf) | i1-IQ2_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-14B-Cobalt2-i1-GGUF/resolve/main/Qwen3-14B-Cobalt2.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
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 -->
|
Kenazin/bert-matching | Kenazin | 2025-05-31T01:33:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-31T01:33:42Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: bert-matching
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. -->
# bert-matching
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Precision: 0.8082
- Recall: 0.6782
- F1: 0.7375
## 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-05
- train_batch_size: 32
- eval_batch_size: 32
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.4114 | 1.0 | 185 | 0.3650 | 0.2857 | 0.0077 | 0.0149 |
| 0.3245 | 2.0 | 370 | 0.2764 | 0.7457 | 0.4943 | 0.5945 |
| 0.2541 | 3.0 | 555 | 0.2453 | 0.6235 | 0.8123 | 0.7055 |
| 0.2104 | 4.0 | 740 | 0.2520 | 0.6339 | 0.8161 | 0.7136 |
| 0.1772 | 5.0 | 925 | 0.2294 | 0.7811 | 0.6973 | 0.7368 |
| 0.1558 | 6.0 | 1110 | 0.2349 | 0.7649 | 0.7356 | 0.75 |
| 0.1444 | 7.0 | 1295 | 0.2626 | 0.7965 | 0.6897 | 0.7392 |
| 0.1331 | 8.0 | 1480 | 0.2692 | 0.7755 | 0.7280 | 0.7510 |
| 0.1263 | 9.0 | 1665 | 0.2829 | 0.7957 | 0.7011 | 0.7454 |
| 0.1194 | 10.0 | 1850 | 0.2914 | 0.8082 | 0.6782 | 0.7375 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
Runware/hidream-i1-full | Runware | 2025-05-31T00:56:47Z | 340 | 1 | diffusers | [
"diffusers",
"safetensors",
"image-generation",
"HiDream.ai",
"text-to-image",
"en",
"license:mit",
"diffusers:HiDreamImagePipeline",
"region:us"
] | text-to-image | 2025-04-23T20:45:28Z | ---
license: mit
tags:
- image-generation
- HiDream.ai
language:
- en
pipeline_tag: text-to-image
library_name: diffusers
---
`HiDream-I1` is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.
<span style="color: #FF5733; font-weight: bold">For more features and to experience the full capabilities of our product, please visit [https://vivago.ai/](https://vivago.ai/).</span>
## Key Features
- โจ **Superior Image Quality** - Produces exceptional results across multiple styles including photorealistic, cartoon, artistic, and more. Achieves state-of-the-art HPS v2.1 score, which aligns with human preferences.
- ๐ฏ **Best-in-Class Prompt Following** - Achieves industry-leading scores on GenEval and DPG benchmarks, outperforming all other open-source models.
- ๐ **Open Source** - Released under the MIT license to foster scientific advancement and enable creative innovation.
- ๐ผ **Commercial-Friendly** - Generated images can be freely used for personal projects, scientific research, and commercial applications.
## Quick Start
Please make sure you have installed [Flash Attention](https://github.com/Dao-AILab/flash-attention). We recommend CUDA version 12.4 for the manual installation.
```
pip install -r requirements.txt
```
Clone the GitHub repo:
```
git clone https://github.com/HiDream-ai/HiDream-I1
```
Then you can run the inference scripts to generate images:
```python
# For full model inference
python ./inference.py --model_type full
# For distilled dev model inference
python ./inference.py --model_type dev
# For distilled fast model inference
python ./inference.py --model_type fast
```
> **Note:** The inference script will automatically download `meta-llama/Meta-Llama-3.1-8B-Instruct` model files. If you encounter network issues, you can download these files ahead of time and place them in the appropriate cache directory to avoid download failures during inference.
## Gradio Demo
We also provide a Gradio demo for interactive image generation. You can run the demo with:
```python
python gradio_demo.py
```
## Evaluation Metrics
### DPG-Bench
| Model | Overall | Global | Entity | Attribute | Relation | Other |
|-----------------|-----------|-----------|-----------|-----------|-----------|-----------|
| PixArt-alpha | 71.11 | 74.97 | 79.32 | 78.60 | 82.57 | 76.96 |
| SDXL | 74.65 | 83.27 | 82.43 | 80.91 | 86.76 | 80.41 |
| DALL-E 3 | 83.50 | 90.97 | 89.61 | 88.39 | 90.58 | 89.83 |
| Flux.1-dev | 83.79 | 85.80 | 86.79 | 89.98 | 90.04 | 89.90 |
| SD3-Medium | 84.08 | 87.90 | 91.01 | 88.83 | 80.70 | 88.68 |
| Janus-Pro-7B | 84.19 | 86.90 | 88.90 | 89.40 | 89.32 | 89.48 |
| CogView4-6B | 85.13 | 83.85 | 90.35 | 91.17 | 91.14 | 87.29 |
| **HiDream-I1** | **85.89**| 76.44 | 90.22 | 89.48 | 93.74 | 91.83 |
### GenEval
| Model | Overall | Single Obj. | Two Obj. | Counting | Colors | Position | Color attribution |
|-----------------|----------|-------------|----------|----------|----------|----------|-------------------|
| SDXL | 0.55 | 0.98 | 0.74 | 0.39 | 0.85 | 0.15 | 0.23 |
| PixArt-alpha | 0.48 | 0.98 | 0.50 | 0.44 | 0.80 | 0.08 | 0.07 |
| Flux.1-dev | 0.66 | 0.98 | 0.79 | 0.73 | 0.77 | 0.22 | 0.45 |
| DALL-E 3 | 0.67 | 0.96 | 0.87 | 0.47 | 0.83 | 0.43 | 0.45 |
| CogView4-6B | 0.73 | 0.99 | 0.86 | 0.66 | 0.79 | 0.48 | 0.58 |
| SD3-Medium | 0.74 | 0.99 | 0.94 | 0.72 | 0.89 | 0.33 | 0.60 |
| Janus-Pro-7B | 0.80 | 0.99 | 0.89 | 0.59 | 0.90 | 0.79 | 0.66 |
| **HiDream-I1** | **0.83**| 1.00 | 0.98 | 0.79 | 0.91 | 0.60 | 0.72 |
### HPSv2.1 benchmark
| Model | Averaged | Animation | Concept-art | Painting | Photo |
|-------------------------|----------------|------------|---------------|--------------|------------|
| Stable Diffusion v2.0 | 26.38 | 27.09 | 26.02 | 25.68 | 26.73 |
| Midjourney V6 | 30.29 | 32.02 | 30.29 | 29.74 | 29.10 |
| SDXL | 30.64 | 32.84 | 31.36 | 30.86 | 27.48 |
| Dall-E3 | 31.44 | 32.39 | 31.09 | 31.18 | 31.09 |
| SD3 | 31.53 | 32.60 | 31.82 | 32.06 | 29.62 |
| Midjourney V5 | 32.33 | 34.05 | 32.47 | 32.24 | 30.56 |
| CogView4-6B | 32.31 | 33.23 | 32.60 | 32.89 | 30.52 |
| Flux.1-dev | 32.47 | 33.87 | 32.27 | 32.62 | 31.11 |
| stable cascade | 32.95 | 34.58 | 33.13 | 33.29 | 30.78 |
| **HiDream-I1** | **33.82** | 35.05 | 33.74 | 33.88 | 32.61 |
## License Agreement
The Transformer models in this repository are licensed under the MIT License. The VAE is from `FLUX.1 [schnell]`, and the text encoders from `google/t5-v1_1-xxl` and `meta-llama/Meta-Llama-3.1-8B-Instruct`. Please follow the license terms specified for these components. You own all content you create with this model. You can use your generated content freely, but you must comply with this license agreement. You are responsible for how you use the models. Do not create illegal content, harmful material, personal information that could harm others, false information, or content targeting vulnerable groups.
## Acknowledgements
- The VAE component is from `FLUX.1 [schnell]`, licensed under Apache 2.0.
- The text encoders are from `google/t5-v1_1-xxl` (licensed under Apache 2.0) and `meta-llama/Meta-Llama-3.1-8B-Instruct` (licensed under the Llama 3.1 Community License Agreement). |
Triangle104/Seshat-Qwen3-8B-Q5_K_M-GGUF | Triangle104 | 2025-05-31T00:53:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"fr",
"en",
"dataset:MaatAI/AfricansHistoryBooksArticlesQA",
"base_model:MaatAI/Seshat-Qwen3-8B",
"base_model:quantized:MaatAI/Seshat-Qwen3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T00:52:54Z | ---
license: apache-2.0
datasets:
- MaatAI/AfricansHistoryBooksArticlesQA
language:
- fr
- en
base_model: MaatAI/Seshat-Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Seshat-Qwen3-8B-Q5_K_M-GGUF
This model was converted to GGUF format from [`MaatAI/Seshat-Qwen3-8B`](https://huggingface.co/MaatAI/Seshat-Qwen3-8B) 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/MaatAI/Seshat-Qwen3-8B) for more details on the model.
---
Seshat is a large language model fine-tuned from Qwen/Qwen3-8B to specialize in question answering related to African History. The model aims to provide informative and contextually relevant answers based on the knowledge embedded in its training data.
This model is designed to understand and generate text in multiple languages including English, French, Swahili, and Yoruba, making historical information about Africa more accessible.
---
## 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/Seshat-Qwen3-8B-Q5_K_M-GGUF --hf-file seshat-qwen3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Seshat-Qwen3-8B-Q5_K_M-GGUF --hf-file seshat-qwen3-8b-q5_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 Triangle104/Seshat-Qwen3-8B-Q5_K_M-GGUF --hf-file seshat-qwen3-8b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Seshat-Qwen3-8B-Q5_K_M-GGUF --hf-file seshat-qwen3-8b-q5_k_m.gguf -c 2048
```
|
Mawdistical/Squelching-Fantasies-qw3-14B-GGUF | Mawdistical | 2025-05-31T00:51:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"nsfw",
"explicit",
"roleplay",
"mixed-AI",
"furry",
"Furry",
"text-generation",
"en",
"base_model:Mawdistical/Squelching-Fantasies-qw3-14B",
"base_model:quantized:Mawdistical/Squelching-Fantasies-qw3-14B",
"license:cc-by-nd-4.0",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2025-05-29T05:14:37Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/qbA_cY0sSZtinMMIWmh1C.png
language:
- en
license: cc-by-nd-4.0
license_link: https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
inference: false
tags:
- nsfw
- explicit
- roleplay
- mixed-AI
- furry
- Furry
pipeline_tag: text-generation
library_name: transformers
base_model: Mawdistical/Squelching-Fantasies-qw3-14B
base_model_relation: quantized
quantized_by: ArtusDev
---
<div style="background-color: #000000; color: #FFFFFF; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #FFFFFF; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Squelching-Fantasies
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/qbA_cY0sSZtinMMIWmh1C.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #fffb29;">
<h3 style="color: #FFFFFF; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #FFFFFF; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #fffb29; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #fffb29; color: #000000; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #FFFFFF;">
<p>
<em>
The wildcard Collection. From Drone like servitude to outright macabre intentions, Squelching Fantasies does it all. Choose your poison dear~
</em>
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #fffb29; margin: 25px 0;">
<h2 style="color: #FFFFFF; font-size: 1.25em; border-bottom: 1px solid #fffb29; padding-bottom: 7px;">โง Browse the whole collection</h2>
<ul>
<li><a href="https://huggingface.co/collections/Mawdistical/squelching-fantasies-68364e0195cf2ae286b82e8c" style="color: #fffb29; text-decoration: underline;">All Squelching Fantasies Models</a></li>
</ul>
<hr style="border: 0; height: 1px; background-color: #fffb29; margin: 25px 0;">
<h2 style="color: #FFFFFF; font-size: 1.25em; border-bottom: 1px solid #fffb29; padding-bottom: 7px;">โง Recommended Settings</h2>
<ul>
<li><strong style="color: #FFFFFF;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #FFFFFF;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #FFFFFF;">DRY Settings</strong> (optional):
<ul>
<li style="color: #FFFFFF;">Multiplier: 0.75-0.85</li>
<li style="color: #FFFFFF;">Base: 1.8</li>
<li style="color: #FFFFFF;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #fffb29; margin: 25px 0;">
<h2 style="color: #FFFFFF; font-size: 1.2em; border-bottom: 1px solid #fffb29; padding-bottom: 7px;">โง Credits</h2>
<ul>
<li><strong style="color: #FFFFFF;">Model Author</strong>: <a href="https://vyvan.se" style="color: #fffb29; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #FFFFFF;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #fffb29;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #fffb29;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #fffb29;">ALLURA-ORG</a></li>
</ul>
</li>
<li><strong style="color: #FFFFFF;">Additional Credit</strong>:
<ul>
<li><a href="https://huggingface.co/xtristan" style="color: #fffb29; text-decoration: underline;">@xtristan</a></li>
<li><a href="https://huggingface.co/Steelskull" style="color: #fffb29; text-decoration: underline;">@Steelskull</a></li>
<li><a href="https://huggingface.co/Sao10K" style="color: #fffb29; text-decoration: underline;">@Sao10K</a></li>
</ul>
</li>
</ul>
<p style="color: #FFFFFF; font-size:1em; margin-top:20px;">
<strong style="color: #FFFFFF;">License:</strong>
<a href="https://creativecommons.org/licenses/by-nd/4.0/deed.en" style="color: #fffb29; text-decoration: underline;">CC BY-ND 4.0</a>
</p>
<p style="color: #FFFFFF; font-size: 1em; margin-top:17px;">
This release is possible thanks to compute from
<a href="https://Shuttleai.com" style="color:#fffb29; text-decoration:underline;">Shuttleai.com</a>
</p>
<hr style="border: 0; height: 1px; background-color: #fffb29; margin: 25px 0;">
<h2 style="color: #FFFFFF; font-size: 1.2em; border-bottom: 1px solid #fffb29; padding-bottom: 7px;">โง Socials</h2>
<ul>
<li>Join our official Discord server <a href="https://discord.gg/aU3a5phBQD" style="color:#fffb29; text-decoration:underline;">Here</a></li>
</ul>
</div> |
Triangle104/Seshat-Qwen3-8B-Q4_K_S-GGUF | Triangle104 | 2025-05-31T00:48:06Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"fr",
"en",
"dataset:MaatAI/AfricansHistoryBooksArticlesQA",
"base_model:MaatAI/Seshat-Qwen3-8B",
"base_model:quantized:MaatAI/Seshat-Qwen3-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-31T00:31:52Z | ---
license: apache-2.0
datasets:
- MaatAI/AfricansHistoryBooksArticlesQA
language:
- fr
- en
base_model: MaatAI/Seshat-Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# Triangle104/Seshat-Qwen3-8B-Q4_K_S-GGUF
This model was converted to GGUF format from [`MaatAI/Seshat-Qwen3-8B`](https://huggingface.co/MaatAI/Seshat-Qwen3-8B) 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/MaatAI/Seshat-Qwen3-8B) for more details on the model.
---
Seshat is a large language model fine-tuned from Qwen/Qwen3-8B to specialize in question answering related to African History. The model aims to provide informative and contextually relevant answers based on the knowledge embedded in its training data.
This model is designed to understand and generate text in multiple languages including English, French, Swahili, and Yoruba, making historical information about Africa more accessible.
---
## 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/Seshat-Qwen3-8B-Q4_K_S-GGUF --hf-file seshat-qwen3-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Seshat-Qwen3-8B-Q4_K_S-GGUF --hf-file seshat-qwen3-8b-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/Seshat-Qwen3-8B-Q4_K_S-GGUF --hf-file seshat-qwen3-8b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Seshat-Qwen3-8B-Q4_K_S-GGUF --hf-file seshat-qwen3-8b-q4_k_s.gguf -c 2048
```
|
BilateralBusiness/perma_chef_mandil_tex_vino_caballero_1_20250530_2344 | BilateralBusiness | 2025-05-31T00:06:45Z | 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 | 2025-05-30T23:54:44Z | ---
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: perma_chef_mandil_tex_vino_caballero_1_20250530_2344
---
# Perma_Chef_Mandil_Tex_Vino_Caballero_1_20250530_2344
<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 `perma_chef_mandil_tex_vino_caballero_1_20250530_2344` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "perma_chef_mandil_tex_vino_caballero_1_20250530_2344",
"lora_weights": "https://huggingface.co/BilateralBusiness/perma_chef_mandil_tex_vino_caballero_1_20250530_2344/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 [๐งจ 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('BilateralBusiness/perma_chef_mandil_tex_vino_caballero_1_20250530_2344', weight_name='lora.safetensors')
image = pipeline('perma_chef_mandil_tex_vino_caballero_1_20250530_2344').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: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BilateralBusiness/perma_chef_mandil_tex_vino_caballero_1_20250530_2344/discussions) to add images that show off what youโve made with this LoRA.
|
mradermacher/cursed-ds-9b-ep2-i1-GGUF | mradermacher | 2025-05-31T00:03:17Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-31T00:02:47Z | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/allura-forge/cursed-ds-9b-ep2
|
danhtran2mind/finetuning-Real-ESRGAN-anime | danhtran2mind | 2025-05-31T00:00:35Z | 0 | 0 | null | [
"upscale",
"image",
"enhancement",
"anime",
"Real-ESRGAN",
"RealESRGAN",
"image-to-image",
"en",
"base_model:ai-forever/Real-ESRGAN",
"base_model:finetune:ai-forever/Real-ESRGAN",
"license:mit",
"region:us"
] | image-to-image | 2025-05-30T23:28:51Z | ---
license: mit
language:
- en
base_model:
- ai-forever/Real-ESRGAN
pipeline_tag: image-to-image
tags:
- upscale
- image
- enhancement
- anime
- Real-ESRGAN
- RealESRGAN
---
## PSNR Score Comparison: RealESRGAN_x4plus vs. Fine-Tuned Model
*Dataset: Private, 1920x1080, 27052 Anime Images*
*RealESRGAN_x4plus Download: [](https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth)*
| **Model Name** | **Meanโ** | **Std Deviationโ** |
|:------------------:|:---------------:|:------------------:|
| RealESRGAN_x4plus | 36.8437 | 3.4551 |
| Fine-Tuned Model | **36.8695** ๐ | **3.3193** ๐ |
## My Fintuned Model Configuration
```yaml
# general settings
name: finetune_RealESRGAN_anime
model_type: RealESRGANModel
scale: 4
num_gpu: auto
manual_seed: 0
# ----------------- options for synthesizing training data in RealESRGANModel ----------------- #
# USM the ground-truth
l1_gt_usm: True
percep_gt_usm: True
gan_gt_usm: False
# the first degradation process
resize_prob: [0.2, 0.7, 0.1] # up, down, keep
resize_range: [0.15, 1.5]
gaussian_noise_prob: 0.5
noise_range: [1, 30]
poisson_scale_range: [0.05, 3]
gray_noise_prob: 0.4
jpeg_range: [30, 95]
# the second degradation process
second_blur_prob: 0.8
resize_prob2: [0.3, 0.4, 0.3] # up, down, keep
resize_range2: [0.3, 1.2]
gaussian_noise_prob2: 0.5
noise_range2: [1, 25]
poisson_scale_range2: [0.05, 2.5]
gray_noise_prob2: 0.4
jpeg_range2: [30, 95]
gt_size: 256
queue_size: 180
# dataset and data loader settings
datasets:
train:
name: DF2K+OST
type: RealESRGANDataset
dataroot_gt: '/kaggle/input'
meta_info: './datasets/meta_info/meta_info_amultiscale.txt'
io_backend:
type: disk
blur_kernel_size: 21
kernel_list: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
kernel_prob: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
sinc_prob: 0.1
blur_sigma: [0.2, 3]
betag_range: [0.5, 4]
betap_range: [1, 2]
blur_kernel_size2: 21
kernel_list2: ['iso', 'aniso', 'generalized_iso', 'generalized_aniso', 'plateau_iso', 'plateau_aniso']
kernel_prob2: [0.45, 0.25, 0.12, 0.03, 0.12, 0.03]
sinc_prob2: 0.1
blur_sigma2: [0.2, 1.5]
betag_range2: [0.5, 4]
betap_range2: [1, 2]
final_sinc_prob: 0.8
gt_size: 256
use_hflip: True
use_rot: False
# data loader
use_shuffle: true
num_worker_per_gpu: 5
batch_size_per_gpu: 10
dataset_enlarge_ratio: 1
prefetch_mode: ~
# Uncomment these for validation
# val:
# name: validation
# type: PairedImageDataset
# dataroot_gt: path_to_gt
# dataroot_lq: path_to_lq
# io_backend:
# type: disk
# network structures
network_g:
type: RRDBNet
num_in_ch: 3
num_out_ch: 3
num_feat: 64
num_block: 23
num_grow_ch: 32
network_d:
type: UNetDiscriminatorSN
num_in_ch: 3
num_feat: 64
skip_connection: True
# path
path:
# use the pre-trained Real-ESRNet model
pretrain_network_g: ./experiments/pretrained_models/RealESRGAN_x4plus.pth
param_key_g: params_ema
strict_load_g: true
pretrain_network_d: ./experiments/pretrained_models/RealESRGAN_x4plus_netD.pth
param_key_d: params
strict_load_d: true
resume_state: ~
# training settings
train:
ema_decay: 0.999
optim_g:
type: Adam
lr: !!float 1e-4
weight_decay: 0
betas: [0.9, 0.99]
optim_d:
type: Adam
lr: !!float 1e-4
weight_decay: 0
betas: [0.9, 0.99]
scheduler:
type: MultiStepLR
milestones: [1]
gamma: 0.5
total_iter: 60000
warmup_iter: -1 # no warm up
# losses
pixel_opt:
type: L1Loss
loss_weight: 1.0
reduction: mean
# perceptual loss (content and style losses)
perceptual_opt:
type: PerceptualLoss
layer_weights:
# before relu
'conv1_2': 0.1
'conv2_2': 0.1
'conv3_4': 1
'conv4_4': 1
'conv5_4': 1
vgg_type: vgg19
use_input_norm: true
perceptual_weight: !!float 1.0
style_weight: 0
range_norm: false
criterion: l1
# gan loss
gan_opt:
type: GANLoss
gan_type: vanilla
real_label_val: 1.0
fake_label_val: 0.0
loss_weight: !!float 1e-1
net_d_iters: 1
net_d_init_iters: 0
# Uncomment these for validation
# validation settings
# val:
# val_freq: !!float 5e3
# save_img: True
# metrics:
# psnr: # metric name
# type: calculate_psnr
# crop_border: 4
# test_y_channel: false
# logging settings
logger:
print_freq: 100
save_checkpoint_freq: !!float 5e3
use_tb_logger: true
wandb:
project: ~
resume_id: ~
# dist training settings
dist_params:
backend: nccl
port: 29500
``` |
mradermacher/PixelReasoner-WarmStart-i1-GGUF | mradermacher | 2025-05-31T00:00:06Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:TIGER-Lab/PixelReasoner-SFT-Data",
"base_model:TIGER-Lab/PixelReasoner-WarmStart",
"base_model:quantized:TIGER-Lab/PixelReasoner-WarmStart",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-30T23:07:59Z | ---
base_model: TIGER-Lab/PixelReasoner-WarmStart
datasets:
- TIGER-Lab/PixelReasoner-SFT-Data
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/TIGER-Lab/PixelReasoner-WarmStart
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/PixelReasoner-WarmStart-GGUF
## 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/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/PixelReasoner-WarmStart-i1-GGUF/resolve/main/PixelReasoner-WarmStart.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
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 -->
|
stewy33/Llama-3.3-70B-Instruct-Reference-0524_original_pkc_fda_approval-eda1113e | stewy33 | 2025-05-30T23:55:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-05-30T23:53:59Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
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 |
yadapruk/blip3o-thai-checkpoint | yadapruk | 2025-05-30T23:46:28Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"blip3o_qwen",
"region:us"
] | null | 2025-05-30T23:01:38Z | NOTE: This is not a production-ready checkpoint.
```
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="BLIP3o/BLIP3o-Model-4B",
repo_type="model"
)
```
Clone the repo (if you havenโt already) and install the environment:
```
git clone https://github.com/JiuhaiChen/BLIP3o.git
```
Then run inference with
```
python inference.py /path/to/checkpoint
```
|
JasperV13/Yehia-7B-SFT-Reasoning-preview | JasperV13 | 2025-05-30T23:45:56Z | 39 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:Navid-AI/Yehia-7B-preview",
"base_model:finetune:Navid-AI/Yehia-7B-preview",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-28T14:13:37Z | ---
base_model: Navid-AI/Yehia-7B-preview
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** JasperV13
- **License:** apache-2.0
- **Finetuned from model :** Navid-AI/Yehia-7B-preview
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)
|
openfree/flux-chatgpt-ghibli-lora | openfree | 2025-05-30T23:44:33Z | 2,880 | 307 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-03-28T12:45:37Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: 'a boy and a girl looking out of a window with a cat perched on the window
sill. There is a bicycle parked in front of them and a plant with flowers
to the right side of the image. The wall behind them is visible in the background. '
output:
url: samples/1743165934422__000001000_0.jpg
- text: 'Ghibli style futuristic stormtrooper with glossy white armor and a sleek helmet, standing heroically on a lush alien planet, vibrant flowers blooming around, soft sunlight illuminating the scene, a gentle breeze rustling the leaves. The armor reflects the pink and purple hues of the alien sunset, creating an ethereal glow around the figure. [trigger] '
output:
url: samples/image - 2025-03-30T192648.799.webp
- text: 'Ghibli style sky whale transport ship, its metallic skin adorned with traditional Japanese patterns, gliding through cotton candy clouds at sunrise. Small floating gardens hang from its sides, where workers in futuristic kimonos tend to glowing plants. Rainbow auroras shimmer in the background. [trigger]'
output:
url: samples/image - 2025-03-30T192651.808.webp
- text: 'Ghibli style young mechanic girl in a floating workshop, surrounded by hovering tools and glowing mechanical parts, her blue overalls covered in oil stains, tinkering with a semi-transparent robot companion. Magical sparks fly as she works, while floating islands with waterfalls drift past her open workshop window. [trigger]'
output:
url: samples/image - 2025-03-30T193535.240.webp
- text: 'Ghibli style ancient forest guardian robot, covered in moss and flowering vines, sitting peacefully in a crystal-clear lake. Its gentle eyes glow with soft blue light, while bioluminescent dragonflies dance around its weathered metal frame. Ancient tech symbols on its surface pulse with a gentle rhythm. [trigger]'
output:
url: samples/image - 2025-03-30T194608.026.webp
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ghibli
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
---
# flux-chatgpt-ghibli-lora
<Gallery />
## Trigger words
You should use `ghibli` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/openfree/flux-chatgpt-ghibli-lora/tree/main) them in the Files & versions tab.
# Recommended Excellent Spaces Related to Our Model
1) Texto to Ghibli: https://huggingface.co/spaces/aiqtech/FLUX-Ghibli-Studio-LoRA
2) Image(Upload) to Ghibli: https://huggingface.co/spaces/ginigen/FLUX-Open-Ghibli-Studio
3) Texto to Meme: https://huggingface.co/spaces/VIDraft/Open-Meme-Studio
4) Image/Text to Ghible: https://huggingface.co/spaces/seawolf2357/Ghibli-Multilingual-Text-rendering
# Recommended 'POST' Related to Our Model
1) https://huggingface.co/posts/seawolf2357/883323339740165
2) https://huggingface.co/posts/aiqtech/202174985893140
3) https://huggingface.co/posts/openfree/925352420925810
4) https://huggingface.co/posts/ginipick/807578740801859
## Use it with the [๐งจ 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.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/flux-chatgpt-ghibli-lora', weight_name='flux-chatgpt-ghibli-lora.safetensors')
image = pipeline('a boy and a girl looking out of a window with a cat perched on the window sill. There is a bicycle parked in front of them and a plant with flowers to the right side of the image. The wall behind them is visible in the background. ').images[0]
image.save("my_image.png")
```
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)
# community: https://discord.gg/openfreeai |
thdsofia/mcqa_sciq_fast | thdsofia | 2025-05-30T23:33:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T23:32:41Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
YaTharThShaRma999/voices | YaTharThShaRma999 | 2025-05-30T23:32:09Z | 0 | 1 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-02-16T23:12:20Z | ---
license: apache-2.0
---
|
xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF | xTbtyE | 2025-05-30T23:22:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:mergekit-community/mergekit-slerp-ajhtyju",
"base_model:quantized:mergekit-community/mergekit-slerp-ajhtyju",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-30T23:21:44Z | ---
base_model: mergekit-community/mergekit-slerp-ajhtyju
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF
This model was converted to GGUF format from [`mergekit-community/mergekit-slerp-ajhtyju`](https://huggingface.co/mergekit-community/mergekit-slerp-ajhtyju) 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/mergekit-community/mergekit-slerp-ajhtyju) 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 xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF --hf-file mergekit-slerp-ajhtyju-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF --hf-file mergekit-slerp-ajhtyju-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 xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF --hf-file mergekit-slerp-ajhtyju-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo xTbtyE/mergekit-slerp-ajhtyju-Q4_K_M-GGUF --hf-file mergekit-slerp-ajhtyju-q4_k_m.gguf -c 2048
```
|
okuzarabasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grunting_toothy_elk | okuzarabasi | 2025-05-30T23:15:34Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grunting toothy elk",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T04:34:50Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grunting_toothy_elk
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grunting toothy elk
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grunting_toothy_elk
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/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="okuzarabasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grunting_toothy_elk", 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/yalcinhasan425-gensyn/huggingface/runs/8zi3v5xu)
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF | mradermacher | 2025-05-30T23:14:25Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:autumncc/Qwen2.5-VL-7B-VRAG",
"base_model:quantized:autumncc/Qwen2.5-VL-7B-VRAG",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-30T22:25:45Z | ---
base_model: autumncc/Qwen2.5-VL-7B-VRAG
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/autumncc/Qwen2.5-VL-7B-VRAG
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-GGUF
## 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/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-VL-7B-VRAG-i1-GGUF/resolve/main/Qwen2.5-VL-7B-VRAG.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
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 -->
|
paulinusjua/cameroon-meals | paulinusjua | 2025-05-30T22:51:02Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"cameroon_meals",
"feature-extraction",
"image-classification",
"computer-vision",
"custom-model",
"cameroon-food",
"pytorch",
"custom_code",
"en",
"license:mit",
"region:us"
] | image-classification | 2025-05-30T01:45:46Z | ---
license: mit
tags:
- image-classification
- computer-vision
- custom-model
- cameroon-food
- pytorch
library_name: transformers
model_name: cameroon-meals
language: en
datasets: []
---
# ๐จ๐ฒ Cameroon Meals Classifier
...
A PyTorch image classification model trained to identify 37 traditional Cameroonian meals using a ResNet34 backbone.
---
## ๐ฝ๏ธ Classes (37 meals)
- Pepper Soup
- Kati Kati and Njama Njama
- Koki Beans
- Mbongo Tchobi
- Dodo
- Soya
- Chin-Chin
- Groundnut Soup
- Sese Plantains
- Okra Soup
- Puff Puff
- Beignet Haricot
- Egusi Soup
- Yassa Chicken
- Meat Pie
- Kwacoco Bible
- Kondre
- Roasted Plantain and Plum
- Banga Soup
- Ekwang
- Bobolo
- Black Soup
- Cornchaff
- Accra Banana
- Egusi Pudding
- Poulet DG
- Sangah
- Banane Malaxรฉe
- Hot Pot Potatoes
- Groundnut Sweet
- Fish Roll
- Garri with Groundnuts
- Eru
- Ndolรฉ
- Achu
- Jollof Rice
---
## ๐ง Model Details
- Backbone: ResNet50
- Framework: PyTorch
- Format: `model_weights.pth`
- Custom class: `CameroonMealsModel` (see `modeling.py`)
---
## ๐ Usage
```python
from transformers import AutoModel
import torch
model = AutoModel.from_pretrained("paulinusjua/cameroon-meals", trust_remote_code=True)
model.eval()
# Example forward (x: a batch of image tensors)
logits = model(x)
|
supmare/MARK | supmare | 2025-05-30T22:46:33Z | 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 | 2025-05-30T22:20:52Z | ---
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: MARK
---
# Mark
<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 `MARK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MARK",
"lora_weights": "https://huggingface.co/supmare/MARK/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 [๐งจ 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('supmare/MARK', weight_name='lora.safetensors')
image = pipeline('MARK').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: 2013
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/supmare/MARK/discussions) to add images that show off what youโve made with this LoRA.
|
FormlessAI/7674d7e8-9bf1-4caa-b3f6-0122138b0547 | FormlessAI | 2025-05-30T22:32:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:facebook/opt-125m",
"base_model:finetune:facebook/opt-125m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-30T20:54:49Z | ---
base_model: facebook/opt-125m
library_name: transformers
model_name: 7674d7e8-9bf1-4caa-b3f6-0122138b0547
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for 7674d7e8-9bf1-4caa-b3f6-0122138b0547
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m).
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/7674d7e8-9bf1-4caa-b3f6-0122138b0547", 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/uwl2c6ve)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
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}}
}
``` |
SciKnowOrg/YESciEval-BioASQ-Llama-3.1-8B | SciKnowOrg | 2025-05-30T22:30:22Z | 0 | 0 | null | [
"safetensors",
"biomedical",
"BioASQ",
"question-answering",
"en",
"arxiv:2505.14279",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:mit",
"region:us"
] | question-answering | 2025-05-28T12:07:03Z | ---
license: mit
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: question-answering
tags:
- biomedical
- BioASQ
---
<div align="center">
<img src="https://raw.githubusercontent.com/sciknoworg/YESciEval/main/images/logo.png" alt="YESciEval Logo" width="500"/>
<a href="https://github.com/sciknoworg/YESciEval"><img src="https://img.shields.io/badge/GitHub-YESciEval-blue?logo=github" /></a>
</div>
Large Language Models (LLMs) have become pivotal in powering scientific question-answering across modern search engines, yet their evaluation robustness remains largely underexplored. To address this gap, we introduce **YESciEval** โ an open-source framework that leverages fine-grained rubric-based assessments combined with reinforcement learning to reduce optimism bias in LLM evaluators. YESciEval provides a comprehensive library for evaluating the quality of synthesized scientific answers using predefined rubrics and sophisticated LLM-based judgment models. This framework enables you to assess answers on key criteria by utilizing pretrained judges and parsing LLM outputs into structured JSON formats for detailed analysis.
**The `YESciEval-BioASQ-Llama-3.1-8B` is a Biomedical domain judge tuned on the [BioASQ](https://data.uni-hannover.de/dataset/yescieval-corpus) dataset from the BioASQ challenge.**
## Usage
First of all, install the `YESciEval` library via PiP:
```bash
pip install yescieval
```
Get started with YESciEval in just a few lines of code. This guide demonstrates how to initialize inputs, load the judge, and initiate the rubric for evaluation of the answer.
``` python
from yescieval import Readability, BioASQAutoJudge
# Sample papers with following format {"title": "abstract", ... }
papers = {
"A Study on AI": "This paper discusses recent advances in artificial intelligence, including deep learning.",
"Machine Learning Basics": "An overview of supervised learning methods such as decision trees and SVMs.",
"Neural Networks Explained": "Explains backpropagation and gradient descent for training networks.",
"Ethics in AI": "Explores ethical concerns in automated decision-making systems.",
"Applications of AI in Healthcare": "Details how AI improves diagnostics and personalized medicine."
}
# Question and synthesized answer
question = "How is AI used in modern healthcare systems?"
answer = (
"AI is being used in healthcare for diagnosing diseases, predicting patient outcomes, "
"and assisting in treatment planning. It also supports personalized medicine and medical imaging."
)
# Step 1: Create a rubric
rubric = Readability(papers=papers, question=question, answer=answer)
# Step 2: Load a judge model
judge = BioASQAutoJudge()
judge.from_pretrained(token="your_huggingface_token")
# Step 3: Evaluate the answer
result = judge.evaluate(rubric=rubric)
print("Raw Evaluation Output:")
print(result)
```
A total of nine evaluation rubrics were defined as part of the YESciEval test framework and can be used via ``yescieval``. The following simple example shows how to import rubrics in your code:
```python
from yescieval import Informativeness, Correctness, Completeness,
Coherence, Relevancy, Integration,
Cohesion, Readability, Conciseness
```
A complete list of rubrics is available at YESciEval [๐ Rubrics](https://yescieval.readthedocs.io/rubrics.html) page. For more detailed documentation, visit [๐ https://yescieval.readthedocs.io](https://yescieval.readthedocs.io)
## Citation
If you find our work helpful, feel free to give us a cite.
```bibtex
@article{d2025yescieval,
title={YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering},
author={D'Souza, Jennifer and Giglou, Hamed Babaei and M{\"u}nch, Quentin},
journal={arXiv preprint arXiv:2505.14279},
year={2025}
}
``` |
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