modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
onnxmodelzoo/ecaresnet101d_Opset16
|
onnxmodelzoo
| 2025-09-18T06:23:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:23:01Z |
---
language: en
license: apache-2.0
model_name: ecaresnet101d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/eca_resnet33ts_Opset16
|
onnxmodelzoo
| 2025-09-18T06:22:42Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:22:33Z |
---
language: en
license: apache-2.0
model_name: eca_resnet33ts_Opset16.onnx
tags:
- Computer_Vision
---
|
tinman2030/lora-llama3-timed
|
tinman2030
| 2025-09-18T06:21:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T04:19:36Z |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
model_name: lora-llama3-timed
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for lora-llama3-timed
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-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="tinman2030/lora-llama3-timed", 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/minhtanh126-mcgill-university/huggingface/runs/fgfvp703)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.1
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
onnxmodelzoo/eca_nfnet_l1_Opset17
|
onnxmodelzoo
| 2025-09-18T06:21:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:20:59Z |
---
language: en
license: apache-2.0
model_name: eca_nfnet_l1_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/eca_nfnet_l0_Opset17
|
onnxmodelzoo
| 2025-09-18T06:20:32Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:20:18Z |
---
language: en
license: apache-2.0
model_name: eca_nfnet_l0_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/eca_botnext26ts_256_Opset16
|
onnxmodelzoo
| 2025-09-18T06:19:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:19:43Z |
---
language: en
license: apache-2.0
model_name: eca_botnext26ts_256_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dpn98_Opset17
|
onnxmodelzoo
| 2025-09-18T06:19:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:19:02Z |
---
language: en
license: apache-2.0
model_name: dpn98_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dpn92_Opset17
|
onnxmodelzoo
| 2025-09-18T06:18:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:18:09Z |
---
language: en
license: apache-2.0
model_name: dpn92_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dpn68b_Opset18
|
onnxmodelzoo
| 2025-09-18T06:17:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:17:47Z |
---
language: en
license: apache-2.0
model_name: dpn68b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dpn131_Opset18
|
onnxmodelzoo
| 2025-09-18T06:17:28Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:17:04Z |
---
language: en
license: apache-2.0
model_name: dpn131_Opset18.onnx
tags:
- Computer_Vision
---
|
ChenWu98/numina_qwen_2.5_0.5b_sft_teachers_no_reasoning_cluster2_split_0_2048
|
ChenWu98
| 2025-09-18T06:17:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:Qwen/Qwen2.5-0.5B",
"base_model:finetune:Qwen/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T06:16:23Z |
---
base_model: Qwen/Qwen2.5-0.5B
library_name: transformers
model_name: numina_qwen_2.5_0.5b_sft_teachers_no_reasoning_cluster2_split_0_2048
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for numina_qwen_2.5_0.5b_sft_teachers_no_reasoning_cluster2_split_0_2048
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B).
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="None", 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/chenwu/huggingface/runs/7ffc75j4)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
onnxmodelzoo/dm_nfnet_f3_Opset17
|
onnxmodelzoo
| 2025-09-18T06:09:48Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:07:48Z |
---
language: en
license: apache-2.0
model_name: dm_nfnet_f3_Opset17.onnx
tags:
- Computer_Vision
---
|
Guiniever/ver4
|
Guiniever
| 2025-09-18T06:09:21Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-09-18T04:22:51Z |
---
base_model: unsloth/meta-llama-3.1-8b-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 0.15.2
|
gsjang/ja-llama-3-swallow-8b-instruct-v0.1-x-meta-llama-3-8b-instruct-kv_fuse
|
gsjang
| 2025-09-18T06:07:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1",
"base_model:merge:tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T06:02:45Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1
library_name: transformers
tags:
- mergekit
- merge
---
# ja-llama-3-swallow-8b-instruct-v0.1-x-meta-llama-3-8b-instruct-kv_fuse
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the KV-Fuse (Fisher-bounded OT Memory Merging) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
tokenizer:
source: union
merge_method: kv_fuse
base_model: meta-llama/Meta-Llama-3-8B-Instruct
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters: {}
- model: tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1
parameters: {}
parameters: {}
write_readme: README.md
```
|
kunyoungparkk/Qwen3-Coder-32B-Instruct-VisualSong-DPO
|
kunyoungparkk
| 2025-09-18T06:05:47Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"dpo",
"lora",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-Coder-30B-A3B-Instruct",
"region:us"
] |
text-generation
| 2025-09-18T06:05:35Z |
---
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-Coder-30B-A3B-Instruct
- dpo
- lora
- transformers
- trl
---
# 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 0.17.1
|
onecat-ai/OneCAT-3B
|
onecat-ai
| 2025-09-18T06:04:24Z | 176 | 8 |
transformers
|
[
"transformers",
"safetensors",
"onecat",
"arxiv:2509.03498",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-06T12:02:17Z |
---
library_name: transformers
license: apache-2.0
---
# OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation
<div align="center">
<img src="https://github.com/onecat-ai/OneCAT/blob/main/assets/banner.png?raw=true" width="480" alt="onecat" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://arxiv.org/abs/2509.03498" target="_blank" style="margin: 2px;">
<img alt="Arxiv" src="https://img.shields.io/badge/OneCAT-Paper-red?logo=arxiv&logoColor=red" fill-opacity="1" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/onecat-ai/OneCAT-3B" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20OneCAT-Model-yellow" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://onecat-ai.github.io/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://img.shields.io/badge/🏠 OneCAT-Homepage-0A66C2?logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/onecat-ai/onecat" target="_blank" style="margin: 2px;">
<img alt="GitHub Repository" src="https://img.shields.io/badge/OneCAT-Github-181717?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
## Brief Introduction
We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a novel, pure decoder-only transformer architecture. Our framework uniquely eliminates the need for external components such as Vision Transformers (ViT) or vision tokenizer during inference, leading to significant efficiency gains, especially for high-resolution
inputs. This is achieved through a modality-specific Mixture-of-Experts (MoE) structure trained with a single autoregressive (AR) objective, which also natively supports dynamic resolutions. Furthermore, we pioneer a multi-scale visual autoregressive mechanism within the Large Language Model (LLM) that drastically reduces decoding steps compared to diffusion-based methods while maintaining state-of-the-art performance. Our findings demonstrate the powerful potential of pure autoregressive modeling as a sufficient and elegant foundation for unified multimodal intelligence. As a result, OneCAT sets a new performance standard, outperforming existing open-source unified multimodal models across benchmarks for multimodal generation, editing, and understanding.
### Key Features
#### 🌟 Pure Decoder-Only Design
Eliminates external vision encoders and VAE tokenizers during inference, using only a lightweight patch embedding layer for raw image processing.
#### 🌟 Mixture-of-Experts (MoE)
Three specialized FFN experts: Text FFN for language comprehension, Understanding FFN for visual tokens, and Generation FFN for image synthesis.
#### 🌟 Multi-Scale Autoregressive
Pioneer Next Scale Prediction paradigm that generates images coarse-to-fine, drastically reducing generation steps compared to diffusion models.
For more details, please refer to the [***OneCAT Technical Report***](https://arxiv.org/abs/2509.03498).
## Contact
If you have any questions, you can either create issues or contact us by email [email protected]
## ✍️ Citation
```bibtex
@article{Li2025OneCAT,
title = {OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation},
author = {Han Li, Xinyu Peng, Yaoming Wang, Zelin Peng, Xin Chen, Rongxiang Weng, Jingang Wang, Xunliang Cai, Wenrui Dai, Hongkai Xiong},
journal = {arXiv preprint arXiv:2509.03498},
year = {2025}
}
```
## 📜 License
OneCAT is licensed under the Apache 2.0.
|
onnxmodelzoo/dm_nfnet_f1_Opset16
|
onnxmodelzoo
| 2025-09-18T06:02:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T06:01:21Z |
---
language: en
license: apache-2.0
model_name: dm_nfnet_f1_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dla60x_c_Opset17
|
onnxmodelzoo
| 2025-09-18T05:59:39Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:59:34Z |
---
language: en
license: apache-2.0
model_name: dla60x_c_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dla60_Opset17
|
onnxmodelzoo
| 2025-09-18T05:58:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:58:08Z |
---
language: en
license: apache-2.0
model_name: dla60_Opset17.onnx
tags:
- Computer_Vision
---
|
david4096/edam-text-256-64
|
david4096
| 2025-09-18T05:58:14Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-18T05:58:07Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-attention
- gnn-gcn
- medium-ontology
---
# edam-text-256-64
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: EDAM.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 256
- **Dropout**: 0.0
- **Training Date**: 2025-09-18
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 124.1 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Attention mechanism learns to weight text vs ontological information
**Embedding Flow:**
- Text: 384 dimensions → 256 hidden → 64 output
- Structure: 3511 concepts → GNN → 64 output
- Fusion: attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('edam-text-256-64')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Fusion Method: attention
Attention-based fusion that learns to focus on relevant embedding components
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={Your Name},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
onnxmodelzoo/dla60_Opset16
|
onnxmodelzoo
| 2025-09-18T05:58:08Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:57:58Z |
---
language: en
license: apache-2.0
model_name: dla60_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dla46x_c_Opset18
|
onnxmodelzoo
| 2025-09-18T05:57:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:57:53Z |
---
language: en
license: apache-2.0
model_name: dla46x_c_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dla34_Opset18
|
onnxmodelzoo
| 2025-09-18T05:57:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:57:19Z |
---
language: en
license: apache-2.0
model_name: dla34_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/dla169_Opset18
|
onnxmodelzoo
| 2025-09-18T05:57:01Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:56:43Z |
---
language: en
license: apache-2.0
model_name: dla169_Opset18.onnx
tags:
- Computer_Vision
---
|
masjiii/my-sentiment-analysis-model-demo
|
masjiii
| 2025-09-18T05:56:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T05:56:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
FutureDM/FDM
|
FutureDM
| 2025-09-18T05:54:44Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:54:44Z |
---
license: apache-2.0
---
|
preetha21res/t5-question-generator-phase1
|
preetha21res
| 2025-09-18T05:54:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:53:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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]
|
BARKEM/qwen3-8b-4bit-eko-lora-conversation
|
BARKEM
| 2025-09-18T05:53:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:46:58Z |
---
base_model: unsloth/qwen3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** BARKEM
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-8b-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)
|
onnxmodelzoo/densenet201_Opset17
|
onnxmodelzoo
| 2025-09-18T05:53:26Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:53:17Z |
---
language: en
license: apache-2.0
model_name: densenet201_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/densenet201_Opset16
|
onnxmodelzoo
| 2025-09-18T05:53:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:53:06Z |
---
language: en
license: apache-2.0
model_name: densenet201_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/densenet169_Opset17
|
onnxmodelzoo
| 2025-09-18T05:52:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:52:49Z |
---
language: en
license: apache-2.0
model_name: densenet169_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/densenet161_Opset18
|
onnxmodelzoo
| 2025-09-18T05:52:39Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:52:26Z |
---
language: en
license: apache-2.0
model_name: densenet161_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/densenet161_Opset16
|
onnxmodelzoo
| 2025-09-18T05:52:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:52:02Z |
---
language: en
license: apache-2.0
model_name: densenet161_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/densenet121_Opset17
|
onnxmodelzoo
| 2025-09-18T05:51:53Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:51:46Z |
---
language: en
license: apache-2.0
model_name: densenet121_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit3_small_patch16_384_in21ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:51:38Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:51:28Z |
---
language: en
license: apache-2.0
model_name: deit3_small_patch16_384_in21ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit3_small_patch16_224_Opset16
|
onnxmodelzoo
| 2025-09-18T05:51:07Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:50:56Z |
---
language: en
license: apache-2.0
model_name: deit3_small_patch16_224_Opset16.onnx
tags:
- Computer_Vision
---
|
ChenWu98/numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_1_2048
|
ChenWu98
| 2025-09-18T05:50:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-1.5B",
"base_model:finetune:Qwen/Qwen2.5-1.5B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T05:49:00Z |
---
base_model: Qwen/Qwen2.5-1.5B
library_name: transformers
model_name: numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_1_2048
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for numina_qwen_2.5_sft_teachers_no_reasoning_cluster2_split_1_2048
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B).
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="None", 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/chenwu/huggingface/runs/z1mrps56)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.51.1
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
onnxmodelzoo/deit3_small_patch16_224_in21ft1k_Opset17
|
onnxmodelzoo
| 2025-09-18T05:50:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:50:34Z |
---
language: en
license: apache-2.0
model_name: deit3_small_patch16_224_in21ft1k_Opset17.onnx
tags:
- Computer_Vision
---
|
husjfry/blockassist-bc-climbing_pouncing_dragonfly_1758174478
|
husjfry
| 2025-09-18T05:49:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"climbing pouncing dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T05:48:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- climbing pouncing dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnxmodelzoo/deit3_large_patch16_384_in21ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:45:44Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:44:15Z |
---
language: en
license: apache-2.0
model_name: deit3_large_patch16_384_in21ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
luckeciano/Qwen-2.5-7B-DrGRPO-SGD-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2281
|
luckeciano
| 2025-09-18T05:45:30Z | 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
| 2025-09-18T01:23:16Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-SGD-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2281
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-SGD-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2281
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-DrGRPO-SGD-FisherMaskToken-1e-8-HessianMaskToken-5e-4-v3_2281", 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/v322g0tx)
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: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound
|
Intel
| 2025-09-18T05:44:44Z | 722 | 11 | null |
[
"safetensors",
"qwen3_next",
"text-generation",
"conversational",
"arxiv:2309.05516",
"base_model:Qwen/Qwen3-Next-80B-A3B-Instruct",
"base_model:quantized:Qwen/Qwen3-Next-80B-A3B-Instruct",
"license:apache-2.0",
"4-bit",
"auto-round",
"region:us"
] |
text-generation
| 2025-09-12T11:44:56Z |
---
base_model:
- Qwen/Qwen3-Next-80B-A3B-Instruct
pipeline_tag: text-generation
license: apache-2.0
---
## Model Details
This model is a mixed int4 model with group_size 128 and symmetric quantization of [Qwen/Qwen3-Next-80B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct) generated by [intel/auto-round](https://github.com/intel/auto-round) **via RTN(no algorithm tuning)**.
Non expert layers are fallback to 8 bits. Please refer to Section Generate the model for more details.
Please follow the license of the original model.
## How To Use
For vllm, this pr is required https://github.com/vllm-project/vllm/pull/24818
### INT4 Inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
dtype="auto",
device_map="auto",
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512,
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
content = tokenizer.decode(output_ids, skip_special_tokens=True)
print("content:", content)
"""
content: A large language model (LLM) is a type of artificial intelligence system trained on vast amounts of text data to understand and generate human-like language. These models, such as GPT, PaLM, or LLaMA, use deep learning architectures—typically based on the transformer network—to predict the next word in a sequence, enabling them to answer questions, write essays, translate languages, and even code. LLMs learn patterns, context, and relationships in language without explicit programming, making them versatile tools for a wide range of natural language tasks. Their scale—often with billions or trillions of parameters—allows them to capture nuanced linguistic features, though they also require significant computational resources and raise important ethical and safety considerations.
"""
```
### Generate the model
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
from auto_round import AutoRound
model_name = "Qwen/Qwen3-Next-80B-A3B-Instruct"
layer_config = {}
for n, m in model.named_modules():
if isinstance(m, torch.nn.Linear):
if "expert" in n and "shared_experts" not in n:
layer_config[n] = {"bits": 4}
print(n, 4)
elif n != "lm_head":
layer_config[n] = {"bits": 8}
print(n, 8)
autoround = AutoRound(model_name, iters=0, layer_config=layer_config)
autoround.quantize_and_save(format="auto_round", output_dir="tmp_autoround")
```
## Evaluate Results
| benchmark | n-shot | backend | Intel/Qwen3-Next-80B-A3B-Instruct-int4-mixed-AutoRound | Qwen/Qwen3-Next-80B-A3B-Instruct |
| :-------: | :----: | :-----: | :----------------------------------------------------: | :------------------------------: |
| gsm8k | 5 | vllm | 0.8393 | 0.8074 |
| mmlu_pro | 5 | vllm | 0.7630 | 0.7621 |
## Ethical Considerations and Limitations
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.
## Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
- [Intel Neural Compressor](https://github.com/intel/neural-compressor)
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
## Cite
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
[arxiv](https://arxiv.org/abs/2309.05516) [github](https://github.com/intel/auto-round)
|
onnxmodelzoo/deit3_large_patch16_224_in21ft1k_Opset17
|
onnxmodelzoo
| 2025-09-18T05:39:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:38:34Z |
---
language: en
license: apache-2.0
model_name: deit3_large_patch16_224_in21ft1k_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit3_base_patch16_384_Opset18
|
onnxmodelzoo
| 2025-09-18T05:37:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:36:39Z |
---
language: en
license: apache-2.0
model_name: deit3_base_patch16_384_Opset18.onnx
tags:
- Computer_Vision
---
|
mradermacher/Mystic-Rune-v2-12B-i1-GGUF
|
mradermacher
| 2025-09-18T05:36:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Vortex5/Mystic-Rune-v2-12B",
"base_model:quantized:Vortex5/Mystic-Rune-v2-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-09-17T20:46:00Z |
---
base_model: Vortex5/Mystic-Rune-v2-12B
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/Vortex5/Mystic-Rune-v2-12B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mystic-Rune-v2-12B-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-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/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mystic-Rune-v2-12B-i1-GGUF/resolve/main/Mystic-Rune-v2-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | 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 -->
|
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e5_step_70_final
|
zjhhhh
| 2025-09-18T05:36:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:35:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/deit3_base_patch16_384_in21ft1k_Opset17
|
onnxmodelzoo
| 2025-09-18T05:35:42Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:35:15Z |
---
language: en
license: apache-2.0
model_name: deit3_base_patch16_384_in21ft1k_Opset17.onnx
tags:
- Computer_Vision
---
|
ShourenWSR/HT-phase_scale-Llama-140k-phase2
|
ShourenWSR
| 2025-09-18T05:35:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:29:02Z |
---
library_name: transformers
license: other
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Llama_phase2_140k
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. -->
# Llama_phase2_140k
This model is a fine-tuned version of [./saves/2phases/Llama_phase1_140k](https://huggingface.co/./saves/2phases/Llama_phase1_140k) on the phase2_140k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 12
- total_train_batch_size: 24
- total_eval_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.5.1+cu124
- Datasets 2.19.1
- Tokenizers 0.21.1
|
onnxmodelzoo/deit3_base_patch16_384_in21ft1k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:35:14Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:34:44Z |
---
language: en
license: apache-2.0
model_name: deit3_base_patch16_384_in21ft1k_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit3_base_patch16_224_Opset17
|
onnxmodelzoo
| 2025-09-18T05:34:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:33:49Z |
---
language: en
license: apache-2.0
model_name: deit3_base_patch16_224_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit3_base_patch16_224_in21ft1k_Opset18
|
onnxmodelzoo
| 2025-09-18T05:33:48Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:33:23Z |
---
language: en
license: apache-2.0
model_name: deit3_base_patch16_224_in21ft1k_Opset18.onnx
tags:
- Computer_Vision
---
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.01_1280_5
|
winnieyangwannan
| 2025-09-18T05:33:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:32:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
kewu93/skywork-medarena-lora-v1
|
kewu93
| 2025-09-18T05:33:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"lora",
"medical",
"reward-model",
"medarena",
"base_model:Skywork/Skywork-Reward-V2-Llama-3.1-8B",
"base_model:adapter:Skywork/Skywork-Reward-V2-Llama-3.1-8B",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:33:04Z |
---
license: apache-2.0
base_model: Skywork/Skywork-Reward-V2-Llama-3.1-8B
tags:
- peft
- lora
- medical
- reward-model
- medarena
library_name: peft
---
# Skywork MedArena LoRA Adapter
This is a LoRA (Low-Rank Adaptation) adapter trained on the MedArena dataset for the Skywork Reward V2 Llama 3.1 8B model.
## Model Details
- **Base Model**: Skywork/Skywork-Reward-V2-Llama-3.1-8B
- **Training Dataset**: kewu93/MedArena-0909
- **Training Epochs**: 10
- **LoRA Rank (r)**: 16
- **LoRA Alpha**: 32
- **Max Length**: 2048
- **Best Step**: 0
## Usage
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
base_model = AutoModelForSequenceClassification.from_pretrained(
"Skywork/Skywork-Reward-V2-Llama-3.1-8B",
num_labels=1,
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Skywork/Skywork-Reward-V2-Llama-3.1-8B")
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "kewu93/skywork-medarena-lora-v1")
# Use for inference
inputs = tokenizer("Your text here", return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
reward_score = outputs.logits.item()
```
## Training Details
This adapter was trained using LoRA fine-tuning on medical preference data from the MedArena dataset. The model learns to score medical responses and prefer higher-quality medical advice.
## Files
- `adapter_config.json`: LoRA adapter configuration
- `adapter_model.safetensors`: LoRA adapter weights
- `tokenizer.json`, `tokenizer_config.json`: Tokenizer files
|
Convert411/seanmayaiavatar
|
Convert411
| 2025-09-18T05:33:12Z | 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
| 2025-09-18T04:47:30Z |
---
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: Sean
---
# Seanmayaiavatar
<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 `Sean` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sean",
"lora_weights": "https://huggingface.co/Convert411/seanmayaiavatar/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Convert411/seanmayaiavatar', weight_name='lora.safetensors')
image = pipeline('Sean').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: 3333
- Learning rate: 0.0004
- LoRA rank: 40
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Convert411/seanmayaiavatar/discussions) to add images that show off what you’ve made with this LoRA.
|
onnxmodelzoo/deit_tiny_patch16_224_Opset18
|
onnxmodelzoo
| 2025-09-18T05:32:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:32:21Z |
---
language: en
license: apache-2.0
model_name: deit_tiny_patch16_224_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/deit_small_distilled_patch16_224_Opset17
|
onnxmodelzoo
| 2025-09-18T05:31:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:31:24Z |
---
language: en
license: apache-2.0
model_name: deit_small_distilled_patch16_224_Opset17.onnx
tags:
- Computer_Vision
---
|
ybkim95/output_epochs_Qwen_Qwen3-8B
|
ybkim95
| 2025-09-18T05:29:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-8B",
"base_model:finetune:Qwen/Qwen3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-15T06:43:55Z |
---
base_model: Qwen/Qwen3-8B
library_name: transformers
model_name: output_epochs_Qwen_Qwen3-8B
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for output_epochs_Qwen_Qwen3-8B
This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
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="ybkim95/output_epochs_Qwen_Qwen3-8B", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.22.0.dev0
- Transformers: 4.55.0
- Pytorch: 2.8.0
- Datasets: 3.3.2
- Tokenizers: 0.21.4
## 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}}
}
```
|
onnxmodelzoo/cspresnext50_Opset16
|
onnxmodelzoo
| 2025-09-18T05:28:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:28:44Z |
---
language: en
license: apache-2.0
model_name: cspresnext50_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cspdarknet53_Opset16
|
onnxmodelzoo
| 2025-09-18T05:27:46Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:27:34Z |
---
language: en
license: apache-2.0
model_name: cspdarknet53_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3sedarknet_x_Opset16
|
onnxmodelzoo
| 2025-09-18T05:27:20Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:27:05Z |
---
language: en
license: apache-2.0
model_name: cs3sedarknet_x_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3sedarknet_l_Opset16
|
onnxmodelzoo
| 2025-09-18T05:26:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:26:44Z |
---
language: en
license: apache-2.0
model_name: cs3sedarknet_l_Opset16.onnx
tags:
- Computer_Vision
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758173136
|
schooncestiaa
| 2025-09-18T05:26:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-18T05:26:35Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-360
|
reinforce-flow
| 2025-09-18T05:26:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:25:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/cs3darknet_x_Opset17
|
onnxmodelzoo
| 2025-09-18T05:25:01Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:24:46Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_x_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_m_Opset17
|
onnxmodelzoo
| 2025-09-18T05:24:23Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:24:17Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_m_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_m_Opset16
|
onnxmodelzoo
| 2025-09-18T05:24:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:24:08Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_m_Opset16.onnx
tags:
- Computer_Vision
---
|
gumperto/Qwen2.5-0.5B-Instruct-emergent-finetune-haiku_samples-all-full-r32
|
gumperto
| 2025-09-18T05:24:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"unsloth",
"conversational",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:00:43Z |
---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-emergent-finetune-haiku_samples-all-full-r32
tags:
- generated_from_trainer
- sft
- trl
- unsloth
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-emergent-finetune-haiku_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gumperto/Qwen2.5-0.5B-Instruct-emergent-finetune-haiku_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/t5ry435y)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e5_step_21
|
zjhhhh
| 2025-09-18T05:24:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:23:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/cs3darknet_l_Opset17
|
onnxmodelzoo
| 2025-09-18T05:23:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:47Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_l_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_l_Opset16
|
onnxmodelzoo
| 2025-09-18T05:23:47Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:36Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_l_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/cs3darknet_focus_m_Opset17
|
onnxmodelzoo
| 2025-09-18T05:23:29Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:23:22Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_focus_m_Opset17.onnx
tags:
- Computer_Vision
---
|
Seizeium/JhaLM
|
Seizeium
| 2025-09-18T05:23:00Z | 0 | 0 | null |
[
"pytorch",
"jhalm",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:02:57Z |
---
license: apache-2.0
---
|
onnxmodelzoo/cs3darknet_focus_l_Opset16
|
onnxmodelzoo
| 2025-09-18T05:22:53Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:22:42Z |
---
language: en
license: apache-2.0
model_name: cs3darknet_focus_l_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/crossvit_18_dagger_408_Opset17
|
onnxmodelzoo
| 2025-09-18T05:22:34Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:22:17Z |
---
language: en
license: apache-2.0
model_name: crossvit_18_dagger_408_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/crossvit_15_dagger_408_Opset16
|
onnxmodelzoo
| 2025-09-18T05:22:04Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:21:52Z |
---
language: en
license: apache-2.0
model_name: crossvit_15_dagger_408_Opset16.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-300
|
reinforce-flow
| 2025-09-18T05:21:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:21:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-280
|
reinforce-flow
| 2025-09-18T05:20:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:19:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
gsjang/ja-llama-3-elyza-jp-8b-x-meta-llama-3-8b-instruct-cons_merge
|
gsjang
| 2025-09-18T05:19:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:merge:elyza/Llama-3-ELYZA-JP-8B",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:merge:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:16:34Z |
---
base_model:
- meta-llama/Meta-Llama-3-8B-Instruct
- elyza/Llama-3-ELYZA-JP-8B
library_name: transformers
tags:
- mergekit
- merge
---
# ja-llama-3-elyza-jp-8b-x-meta-llama-3-8b-instruct-cons_merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the CONS-Merge (Fisher-inspired OT Memory Merge) merge method using [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [elyza/Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
tokenizer:
source: union
merge_method: cons_merge
base_model: meta-llama/Meta-Llama-3-8B-Instruct
models:
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters: {}
- model: elyza/Llama-3-ELYZA-JP-8B
parameters: {}
parameters: {}
write_readme: README.md
```
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-240
|
reinforce-flow
| 2025-09-18T05:17:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:16:21Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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|
zjhhhh/qwen2.5_3B_Instruct_judge_rebel_1e4_step_21
|
zjhhhh
| 2025-09-18T05:15:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:15:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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[More Information Needed]
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|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_pnas_layer_16_4_all_37_0.01_1280_3
|
winnieyangwannan
| 2025-09-18T05:15:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:14:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- 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. -->
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#### Metrics
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### Results
[More Information Needed]
#### Summary
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[More Information Needed]
## Environmental Impact
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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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|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-220
|
reinforce-flow
| 2025-09-18T05:15:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:14:43Z |
---
library_name: transformers
tags: []
---
# 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 🤗 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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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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|
onnxmodelzoo/convnext_tiny_Opset17
|
onnxmodelzoo
| 2025-09-18T05:12:48Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:12:36Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convnext_tiny_in22k_Opset18
|
onnxmodelzoo
| 2025-09-18T05:12:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:12:07Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_in22k_Opset18.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-180
|
reinforce-flow
| 2025-09-18T05:12:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:11:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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<!-- 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
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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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[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_tiny_in22k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:12:06Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:11:49Z |
---
language: en
license: apache-2.0
model_name: convnext_tiny_in22k_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/convnext_small_Opset17
|
onnxmodelzoo
| 2025-09-18T05:11:04Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:10:46Z |
---
language: en
license: apache-2.0
model_name: convnext_small_Opset17.onnx
tags:
- Computer_Vision
---
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-160
|
reinforce-flow
| 2025-09-18T05:10:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:10:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/convnext_small_Opset16
|
onnxmodelzoo
| 2025-09-18T05:10:45Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:10:24Z |
---
language: en
license: apache-2.0
model_name: convnext_small_Opset16.onnx
tags:
- Computer_Vision
---
|
lynn-mikami/wan-testing
|
lynn-mikami
| 2025-09-18T05:09:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-07-18T10:20:30Z |
---
license: apache-2.0
---
|
onnxmodelzoo/convnext_large_Opset18
|
onnxmodelzoo
| 2025-09-18T05:08:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:07:32Z |
---
language: en
license: apache-2.0
model_name: convnext_large_Opset18.onnx
tags:
- Computer_Vision
---
|
JonusNattapong/xauusd-trading-ai-smc
|
JonusNattapong
| 2025-09-18T05:07:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-18T03:47:11Z |
add---
language: en
license: mit
developer: Jonus Nattapong Tapachom
tags:
- trading
- finance
- gold
- xauusd
- forex
- algorithmic-trading
- smart-money-concepts
- smc
- xgboost
- machine-learning
- backtesting
- technical-analysis
- multi-timeframe
- intraday-trading
- high-frequency-trading
datasets:
- yahoo-finance-gc-f
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: xauusd-trading-ai-smc-daily
results:
- task:
type: binary-classification
name: Daily Price Direction Prediction
dataset:
type: yahoo-finance-gc-f
name: Gold Futures (GC=F)
metrics:
- type: accuracy
value: 80.3
name: Accuracy
- type: precision
value: 71
name: Precision (Class 1)
- type: recall
value: 81
name: Recall (Class 1)
- type: f1
value: 76
name: F1-Score
- name: xauusd-trading-ai-smc-15m
results:
- task:
type: binary-classification
name: 15-Minute Price Direction Prediction
dataset:
type: yahoo-finance-gc-f
name: Gold Futures (GC=F)
metrics:
- type: accuracy
value: 77.0
name: Accuracy
- type: precision
value: 76
name: Precision (Class 1)
- type: recall
value: 77
name: Recall (Class 1)
- type: f1
value: 76
name: F1-Score
---
# XAUUSD Multi-Timeframe Trading AI Model
## Files Included
### Core Models
- `trading_model.pkl` - Original daily timeframe XGBoost model (85.4% win rate)
- `trading_model_15m.pkl` - 15-minute intraday model (77% validation accuracy)
- `trading_model_1m.pkl` - 1-minute intraday model (partially trained)
- `trading_model_30m.pkl` - 30-minute intraday model (ready for training)
### Documentation
- `README.md` - This comprehensive model card
- `XAUUSD_Trading_AI_Paper.md` - **Research paper with academic structure, literature review, and methodology**
- `XAUUSD_Trading_AI_Paper.docx` - **Word document version (professional format)**
- `XAUUSD_Trading_AI_Paper.html` - **HTML web version (styled and readable)**
- `XAUUSD_Trading_AI_Paper.tex` - **LaTeX source (for academic publishing)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.md` - **Technical whitepaper with mathematical formulations and implementation details**
- `XAUUSD_Trading_AI_Technical_Whitepaper.docx` - **Word document version (professional format)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.html` - **HTML web version (styled and readable)**
- `XAUUSD_Trading_AI_Technical_Whitepaper.tex` - **LaTeX source (for academic publishing)**
### Performance & Analysis
- `backtest_report.csv` - Daily model yearly backtesting performance results
- `backtest_multi_timeframe_results.csv` - Intraday model backtesting results
- `feature_importance_15m.csv` - 15-minute model feature importance analysis
### Scripts & Tools
- `train_multi_timeframe.py` - Multi-timeframe model training script
- `backtest_multi_timeframe.py` - Intraday model backtesting framework
- `multi_timeframe_summary.py` - Comprehensive performance analysis tool
- `fetch_data.py` - Enhanced data acquisition for multiple timeframes
### Dataset Files
- **Daily Data**: `daily_data.csv`, `processed_daily_data.csv`, `smc_features_dataset.csv`, `X_features.csv`, `y_target.csv`
- **Intraday Data**: `1m_data.csv` (5,204 samples), `15m_data.csv` (3,814 samples), `30m_data.csv` (1,910 samples)
## Recent Enhancements (v2.0)
### Visual Documentation
- **Dataset Flow Diagram**: Complete data processing pipeline from raw Yahoo Finance data to model training
- **Model Architecture Diagram**: XGBoost ensemble structure with decision flow visualization
- **Buy/Sell Workflow Diagram**: End-to-end trading execution process with risk management
### Advanced Formulas & Techniques
- **Position Sizing Formula**: Risk-adjusted position calculation with Kelly Criterion adaptation
- **Risk Metrics**: Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown calculations
- **SMC Techniques**: Advanced Order Block detection with volume profile analysis
- **Dynamic Thresholds**: Market volatility-based prediction threshold adjustment
- **Ensemble Signals**: Multi-source signal confirmation (ML + Technical + SMC)
### Performance Analytics
- **Monthly Performance Heatmap**: Visual representation of returns across all test years
- **Risk-Return Scatter Plot**: Performance comparison across different risk levels
- **Market Regime Analysis**: Performance breakdown by trending vs sideways markets
### Documentation Updates
- **Enhanced Technical Whitepaper**: Added comprehensive visual diagrams and mathematical formulations
- **Enhanced Research Paper**: Added Mermaid diagrams, advanced algorithms, and detailed performance analysis
- **Professional Exports**: Both documents now available in HTML, Word, and LaTeX formats
## Multi-Timeframe Trading System (Latest Addition)
### Overview
The system has been extended to support intraday trading across multiple timeframes, enabling higher-frequency trading strategies while maintaining the proven SMC + technical indicator approach.
### Supported Timeframes
- **1-minute (1m)**: Ultra-short-term scalping opportunities
- **15-minute (15m)**: Short-term swing trading
- **30-minute (30m)**: Medium-term position trading
- **Daily (1d)**: Original baseline model (85.4% win rate)
### Data Acquisition
- **Source**: Yahoo Finance API with enhanced intraday data fetching
- **Limitations**: Historical intraday data restricted (recent periods only)
- **Current Datasets**:
- 1m: 5,204 samples (7 days of recent data)
- 15m: 3,814 samples (60 days of recent data)
- 30m: 1,910 samples (60 days of recent data)
### Model Architecture
- **Base Algorithm**: XGBoost Classifier (same as daily model)
- **Features**: 23 features (technical indicators + SMC elements)
- **Training**: Grid search hyperparameter optimization
- **Validation**: 80/20 train/test split with stratification
### Training Results
- **15m Model**: Successfully trained with 77% validation accuracy
- **Feature Importance**: Technical indicators dominant (SMA_50, EMA_12, BB_lower)
- **Training Status**: 1m model partially trained, 30m model interrupted (available for completion)
### Backtesting Performance
- **Framework**: Backtrader with realistic commission modeling
- **Risk Management**: Fixed stake sizing ($1,000 per trade)
- **15m Results**: -0.83% return with 1 trade (conservative strategy)
- **Analysis**: Models show conservative behavior to avoid overtrading
### Key Insights
- ✅ Successfully scaled daily model architecture to intraday timeframes
- ✅ Technical indicators remain most important across all timeframes
- ✅ Conservative prediction thresholds prevent excessive trading
- ⚠️ Limited historical data affects backtesting statistical significance
- ⚠️ Yahoo Finance API constraints limit comprehensive validation
### Files Added
- `train_multi_timeframe.py` - Multi-timeframe model training script
- `backtest_multi_timeframe.py` - Intraday model backtesting framework
- `multi_timeframe_summary.py` - Comprehensive performance analysis
- `trading_model_15m.pkl` - Trained 15-minute model
- `feature_importance_15m.csv` - Feature importance analysis
- `backtest_multi_timeframe_results.csv` - Backtesting performance data
### Next Steps
1. Complete 30m model training
2. Implement walk-forward optimization
3. Add extended historical data sources
4. Deploy best performing intraday model
5. Compare intraday vs daily performance
## Model Description
This is an AI-powered trading model for XAUUSD (Gold vs US Dollar) futures, trained using Smart Money Concepts (SMC) strategy elements. The model uses machine learning to predict 5-day ahead price movements and generate trading signals with high win rates.
### Key Features
- **Asset**: XAUUSD (Gold Futures)
- **Strategy**: Smart Money Concepts (SMC) with technical indicators
- **Prediction Horizon**: 5-day ahead price direction
- **Model Type**: XGBoost Classifier
- **Accuracy**: 80.3% on test data
- **Win Rate**: 85.4% in backtesting
## Intended Use
This model is designed for:
- Educational purposes in algorithmic trading
- Research on SMC strategies
- Backtesting trading strategies
- Understanding ML applications in financial markets
**⚠️ Warning**: This is not financial advice. Trading involves risk of loss. Use at your own discretion.
## Training Data
- **Source**: Yahoo Finance (GC=F - Gold Futures)
- **Period**: 2000-2020 (excluding recent months for efficiency)
- **Features**: 23 features including:
- Price data (Open, High, Low, Close, Volume)
- Technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands)
- SMC features (Fair Value Gaps, Order Blocks, Recovery patterns)
- Lag features (Close prices from previous days)
- **Target**: Binary classification (1 if price rises in 5 days, 0 otherwise)
- **Dataset Size**: 8,816 samples
- **Class Distribution**: 54% down, 46% up (balanced with scale_pos_weight)
## Performance Metrics
### Model Performance
- **Accuracy**: 80.3%
- **Precision (Class 1)**: 71%
- **Recall (Class 1)**: 81%
- **F1-Score**: 76%
### Backtesting Results (2015-2020)
- **Overall Win Rate**: 85.4%
- **Total Return**: 18.2%
- **Sharpe Ratio**: 1.41
- **Yearly Win Rates**:
- 2015: 62.5%
- 2016: 100.0%
- 2017: 100.0%
- 2018: 72.7%
- 2019: 76.9%
- 2020: 94.1%
## Limitations
- Trained on historical data only (2000-2020)
- May not perform well in unprecedented market conditions
- Requires proper risk management
- No consideration of transaction costs, slippage, or market impact
- Model predictions are probabilistic, not guaranteed
## Usage
### Prerequisites
```python
pip install joblib scikit-learn pandas numpy
```
### Loading the Model
```python
import joblib
import pandas as pd
from sklearn.preprocessing import StandardScaler
# Load model
model = joblib.load('trading_model.pkl')
# Load scalers (you need to recreate or save them)
# ... preprocessing code ...
# Prepare features
features = prepare_features(your_data)
prediction = model.predict(features)
probability = model.predict_proba(features)
```
### Features Required
The model expects 23 features in this order:
1. Close
2. High
3. Low
4. Open
5. Volume
6. SMA_20
7. SMA_50
8. EMA_12
9. EMA_26
10. RSI
11. MACD
12. MACD_signal
13. MACD_hist
14. BB_upper
15. BB_middle
16. BB_lower
17. FVG_Size
18. FVG_Type_Encoded
19. OB_Type_Encoded
20. Recovery_Type_Encoded
21. Close_lag1
22. Close_lag2
23. Close_lag3
## Training Details
- **Algorithm**: XGBoost Classifier
- **Hyperparameters**:
- n_estimators: 200
- max_depth: 7
- learning_rate: 0.2
- scale_pos_weight: 1.17 (for class balancing)
- **Cross-validation**: 3-fold
- **Optimization**: Grid search on hyperparameters
## SMC Strategy Elements
The model incorporates Smart Money Concepts:
- **Fair Value Gaps (FVG)**: Price imbalances between candles
- **Order Blocks (OB)**: Areas of significant buying/selling
- **Recovery Patterns**: Pullbacks in trending markets
## Upload to Hugging Face
To share this model on Hugging Face:
1. Create a Hugging Face account at https://huggingface.co/join
2. Generate an access token at https://huggingface.co/settings/tokens with "Write" permissions
3. Test your token: `python test_token.py YOUR_TOKEN`
4. Upload: `python upload_to_hf.py YOUR_TOKEN`
The script will upload:
- `trading_model.pkl` - The trained XGBoost model
- `README.md` - This model card with metadata
- All dataset files (CSV format)
## Citation
If you use this model in your research, please cite:
```
@misc{xauusd-trading-ai,
title={XAUUSD Trading AI Model with SMC Strategy},
author={AI Trading System},
year={2025},
url={https://huggingface.co/JonusNattapong/xauusd-trading-ai-smc}
}
```
### Academic Paper
For the complete academic research paper with methodology, results, and analysis:
**arXiv Paper**: [XAUUSD Trading AI: A Machine Learning Approach Using Smart Money Concepts](https://arxiv.org/abs/XXXX.XXXXX)
## License
This model is released under the MIT License. See LICENSE file for details.
## Contact
For questions or issues, please open an issue on the Hugging Face repository.
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp_pnas_layer_16_4_all_37_0.0005_1280_3
|
winnieyangwannan
| 2025-09-18T05:06:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:05:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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]
|
Darkhn/L3.3-70B-Animus-V12.0-GGUF
|
Darkhn
| 2025-09-18T05:06:10Z | 40,957 | 0 |
llama.cpp
|
[
"llama.cpp",
"gguf",
"q3-k-s",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-09T01:27:48Z |
---
license: mit
library_name: llama.cpp
tags:
- gguf
- q2-k-s
---
# L3.3-70B-Animus-V12.0-GGUF
GGUF model files for `L3.3-70B-Animus-V12.0`.
This repository contains GGUF models quantized using [`llama.cpp`](https://github.com/ggerganov/llama.cpp).
- **Base Model:** `L3.3-70B-Animus-V12.0`
- **Quantization Methods Processed in this Job:** `Q4_K_M`, `Q4_K_S`, `Q3_K_L`, `Q3_K_M`, `Q3_K_S`, `Q2_K_S`, `Q2_K`
- **Importance Matrix Used:** Yes
This specific upload is for the **`Q2_K_S`** quantization.
|
reinforce-flow/qwen2.5math-1.5b-adaptive-iter-100
|
reinforce-flow
| 2025-09-18T05:05:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:05:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### 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
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#### 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. -->
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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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|
TungCan/tuning-sentiment-abp-pos
|
TungCan
| 2025-09-18T05:03:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"vietnamese",
"sentiment-analysis",
"generated_from_trainer",
"base_model:5CD-AI/Vietnamese-Sentiment-visobert",
"base_model:finetune:5CD-AI/Vietnamese-Sentiment-visobert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-18T05:03:20Z |
---
library_name: transformers
base_model: 5CD-AI/Vietnamese-Sentiment-visobert
tags:
- text-classification
- vietnamese
- sentiment-analysis
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: tuning-sentiment-abp-pos
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. -->
# tuning-sentiment-abp-pos
This model is a fine-tuned version of [5CD-AI/Vietnamese-Sentiment-visobert](https://huggingface.co/5CD-AI/Vietnamese-Sentiment-visobert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5678
- Accuracy: 0.8153
- F1: 0.8137
- Precision: 0.8548
- Recall: 0.8001
## 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-05
- train_batch_size: 32
- eval_batch_size: 64
- 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4963 | 1.0 | 799 | 0.4323 | 0.8203 | 0.8122 | 0.8699 | 0.7992 |
| 0.3918 | 2.0 | 1598 | 0.4477 | 0.8223 | 0.8175 | 0.8720 | 0.7997 |
| 0.3535 | 3.0 | 2397 | 0.4750 | 0.8223 | 0.8157 | 0.8747 | 0.8001 |
| 0.3221 | 4.0 | 3196 | 0.5362 | 0.8176 | 0.8150 | 0.8585 | 0.8001 |
| 0.3041 | 5.0 | 3995 | 0.5678 | 0.8153 | 0.8137 | 0.8548 | 0.8001 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
fuyingw/MELP_Encoder
|
fuyingw
| 2025-09-18T05:03:13Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"melp",
"feature-extraction",
"audio-text-to-text",
"custom_code",
"arxiv:2506.21803",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] |
audio-text-to-text
| 2025-04-15T12:49:00Z |
---
library_name: transformers
tags: []
license: apache-2.0
pipeline_tag: audio-text-to-text
---
# Model Card for MELP
<!-- Provide a quick summary of what the model is/does. -->
MELP (Multi-scale ECG-Language Pretraining) is a novel model presented in the paper "From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining". It aims to overcome limitations in traditional ECG analysis by leveraging hierarchical supervision from ECG-text pairs to align ECG signals with textual reports.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Electrocardiograms (ECGs) play a vital role in monitoring cardiac health and diagnosing heart diseases. However, traditional deep learning approaches for ECG analysis rely heavily on large-scale manual annotations, which are both time-consuming and resource-intensive to obtain. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising alternative, enabling the extraction of robust ECG representations that can be efficiently transferred to various downstream tasks.
MELP introduces a novel Multi-scale ECG-Language Pretraining (MELP) model that fully leverages hierarchical supervision from ECG-text pairs. MELP first pretrains a cardiology-specific language model to enhance its understanding of clinical text. It then applies three levels of cross-modal supervision—at the token, beat, and rhythm levels—to align ECG signals with textual reports, capturing structured information across different time scales. Experimental results demonstrate that MELP outperforms existing SSL methods, underscoring its effectiveness and adaptability across diverse clinical applications.
- **Developed by:** The authors of the paper.
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Multimodal ECG-Language Pretraining Model
- **Language(s) (NLP):** English (clinical text)
- **License:** Apache-2.0
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** The code is available as mentioned in the paper's abstract. Please refer to the paper for the exact URL.
- **Paper [optional]:** [From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining](https://huggingface.co/papers/2506.21803)
- **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. -->
MELP can be directly used for self-supervised learning of robust ECG representations. These representations can be efficiently transferred to various downstream tasks, such as zero-shot ECG classification, linear probing, and other transfer learning applications on ECG data.
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
The model can be fine-tuned for diverse clinical applications, including but not limited to tasks that require aligning ECG signals with textual reports, thereby assisting in cardiac health monitoring and heart disease diagnosis.
### 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.
```python
from transformers import AutoConfig, AutoModel
# This is a placeholder for the actual model ID on the Hugging Face Hub.
# Replace "your_model_id" with the correct model identifier.
model_id = "your_model_id" # e.g., "org/melp-model"
# Load configuration
config = AutoConfig.from_pretrained(model_id)
# Load model
model = AutoModel.from_pretrained(model_id, config=config)
# For detailed usage instructions and examples, please refer to the paper's
# official code repository mentioned in the abstract.
```
## 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:**
```bibtex
@article{zhou2025token,
title={From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining},
author={Zhou, Zijian and Liu, Shikun and Han, Xiao and Liu, Haozhe and Ng, Kam Woh and Xie, Tian and Cong, Yuren and Li, Hang and Xu, Mengmeng and P{\'e}rez-R{\'u}a, Juan-Manuel and Patel, Aditya and Xiang, Tao and Shi, Miaojing and He, Sen},
journal={arXiv preprint arXiv:2506.21803},
year={2025}
}
```
**APA:**
Zhou, Z., Liu, S., Han, X., Liu, H., Ng, K. W., Xie, T., Cong, Y., Li, H., Xu, M., Pérez-Rúa, J.-M., Patel, A., Xiang, T., Shi, M., & He, S. (2025). *From Token to Rhythm: A Multi-Scale Approach for ECG-Language Pretraining*. arXiv preprint arXiv:2506.21803.
## 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]
|
onnxmodelzoo/convnext_large_in22k_Opset16
|
onnxmodelzoo
| 2025-09-18T05:03:12Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T05:02:01Z |
---
language: en
license: apache-2.0
model_name: convnext_large_in22k_Opset16.onnx
tags:
- Computer_Vision
---
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_pnas_layer_16_4_all_37_0.005_1280_5
|
winnieyangwannan
| 2025-09-18T05:02:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T05:00:56Z |
---
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 🤗 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]
|
onnxmodelzoo/convnext_base_Opset17
|
onnxmodelzoo
| 2025-09-18T04:57:46Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-18T04:57:16Z |
---
language: en
license: apache-2.0
model_name: convnext_base_Opset17.onnx
tags:
- Computer_Vision
---
|
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