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rossieRuby/nyayadrishti-minitron-lora | rossieRuby | 2025-06-22T10:59:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:58:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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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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## Model Card Contact
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Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF | Triangle104 | 2025-06-22T10:58:53Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Huihui-MoE-23B-A4B-abliterated",
"base_model:quantized:huihui-ai/Huihui-MoE-23B-A4B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-22T10:20:31Z | ---
license: apache-2.0
base_model: huihui-ai/Huihui-MoE-23B-A4B-abliterated
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-4B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
- llama-cpp
- gguf-my-repo
---
# Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/Huihui-MoE-23B-A4B-abliterated`](https://huggingface.co/huihui-ai/Huihui-MoE-23B-A4B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Huihui-MoE-23B-A4B-abliterated) for more details on the model.
---
Huihui-MoE-23B-A4B-abliterated is a Mixture of Experts (MoE) language model developed by huihui.ai, built upon the huihui-ai/Huihui-Qwen3-4B-abliterated-v2 base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 8 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF --hf-file huihui-moe-23b-a4b-abliterated-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF --hf-file huihui-moe-23b-a4b-abliterated-q3_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF --hf-file huihui-moe-23b-a4b-abliterated-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Huihui-MoE-23B-A4B-abliterated-Q3_K_S-GGUF --hf-file huihui-moe-23b-a4b-abliterated-q3_k_s.gguf -c 2048
```
|
AXERA-TECH/MixFormerV2 | AXERA-TECH | 2025-06-22T10:57:57Z | 30 | 0 | null | [
"onnx",
"Transformer",
"Tracking",
"ONNX",
"en",
"license:mit",
"region:us"
] | null | 2025-04-03T13:58:52Z | ---
license: mit
language:
- en
tags:
- Transformer
- Tracking
- ONNX
---
# MixFormerV2
This version of MixFormerV2 has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 3.4
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through
- [The repo of original](https://github.com/MCG-NJU/MixFormerV2)
- [The repo of AXera Platform](https://github.com/Jordan-5i/ax650_mixformer2_demo), which you can get the detial of guide
- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html)
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
- AX630C
- [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html)
- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM)
- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit)
|Chips|npu1|
|--|--|
|AX650| 11 ms |
|AX630C| 33 ms |
## How to use
Download all files from this repository to the device
```
root@ax650:/mnt/qtang/MixFormerV2# tree -L 1
.
├── ax650
├── car.avi
├── config.json
├── onnx
├── README.md
├── run_mixformer2_axmodel.py
└── run_mixformer2_onnx.py
```
### python env requirement
#### pyaxengine
https://github.com/AXERA-TECH/pyaxengine
```
wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.1rc0/axengine-0.1.1-py3-none-any.whl
pip install axengine-0.1.1-py3-none-any.whl
```
#### others
```
pip install argparse numpy opencv-python glob2
```
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
```
root@ax650:/mnt/qtang/ax650_mixformer2_demo# python3 run_mixformer2_axmodel.py --model-path ax650/mixformer_v2.axmodel --frame-path car.avi -r 10
[INFO] Available providers: ['AxEngineExecutionProvider']
[INFO] Using provider: AxEngineExecutionProvider
[INFO] Chip type: ChipType.MC50
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Engine version: 2.7.2a
[INFO] Model type: 0 (single core)
[INFO] Compiler version: 3.4-dirty 4ff37520-dirty
====================type================= [1079, 482] <class 'list'> <class 'list'>
第一帧初始化完毕！
Video: tracking 246.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Video: tracking 4.0fps
Reached the maximum number of frames (10). Exiting loop.
video: average finale average tracking fps 31.8 fps
root@ax650:/mnt/qtang/ax650_mixformer2_demo#
```
#### Inference with M.2 Accelerator card
[What is M.2 Accelerator card?](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html), Show this DEMO based on Raspberry PI 5.
```
(axcl) axera@raspberrypi:~/samples/MixFormerV2 $ python3 run_mixformer2_axmodel.py --model-path ax650/mixformer_v2.axmodel --frame-path car.avi -r 10
[INFO] Available providers: ['AXCLRTExecutionProvider']
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4-dirty 4ff37520-dirty
====================type================= [1079, 482] <class 'list'> <class 'list'>
第一帧初始化完毕！
Video: tracking 925.0fps
Video: tracking 12.0fps
Video: tracking 12.0fps
Video: tracking 11.0fps
Video: tracking 11.0fps
Video: tracking 11.0fps
Video: tracking 11.0fps
Video: tracking 11.0fps
Video: tracking 10.0fps
Video: tracking 10.0fps
Video: tracking 10.0fps
Reached the maximum number of frames (10). Exiting loop.
video: average finale average tracking fps 114.9 fps
(axcl) axera@raspberrypi:~/samples/MixFormerV2 $
``` |
Mahdikp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_nasty_warthog | Mahdikp | 2025-06-22T10:57:13Z | 57 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am cunning nasty warthog",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T11:46:45Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_nasty_warthog
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am cunning nasty warthog
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_nasty_warthog
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="Mahdikp/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-cunning_nasty_warthog", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rapid_feline_fish | SIGTIR | 2025-06-22T10:56:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rapid feline fish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T14:59:44Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rapid_feline_fish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rapid feline fish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rapid_feline_fish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rapid_feline_fish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gitas/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_silent_starfish | gitas | 2025-06-22T10:55:53Z | 64 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am miniature silent starfish",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-11T12:41:03Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_silent_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am miniature silent starfish
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_silent_starfish
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="gitas/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-miniature_silent_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
HashBrownSelfie/Llama-3.2-1B-Sonnet | HashBrownSelfie | 2025-06-22T10:55:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:48:50Z | ---
base_model: meta-llama/Llama-3.2-1B
library_name: transformers
model_name: Llama-3.2-1B-Sonnet
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for Llama-3.2-1B-Sonnet
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B).
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="HashBrownSelfie/Llama-3.2-1B-Sonnet", 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/kellis8-fitchburg-state-university/Project2_KolbyEllis/runs/yu15goo7)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SoIslam0311/Prine | SoIslam0311 | 2025-06-22T10:55:01Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-3.5-large",
"base_model:adapter:stabilityai/stable-diffusion-3.5-large",
"region:us"
] | text-to-image | 2025-06-22T10:53:59Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/Work2.jpg
base_model: stabilityai/stable-diffusion-3.5-large
instance_prompt: null
---
# Prine
<Gallery />
## Download model
[Download](/SoIslam0311/Prine/tree/main) them in the Files & versions tab.
|
aARSNT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_freckled_hyena | aARSNT | 2025-06-22T10:53:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am energetic freckled hyena",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T01:13:48Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_freckled_hyena
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am energetic freckled hyena
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_freckled_hyena
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="aARSNT/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-energetic_freckled_hyena", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
18-Anabel-Angus-Y-Marco-Antelo-Video/Ultimo.Video.De.Anabel.Angus.Y.Marco.Antelo.Enlace.de.Terabox.Link | 18-Anabel-Angus-Y-Marco-Antelo-Video | 2025-06-22T10:53:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T10:53:20Z | <a href="https://tinyurl.com/Videos-Pinoy?hasinamodi" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Putru7/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver | Putru7 | 2025-06-22T10:53:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am insectivorous shrewd beaver",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T23:37:16Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am insectivorous shrewd beaver
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Putru7/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-insectivorous_shrewd_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Official-mezzo-fun-18-Viral-videos-Clip-XX/FULL.VIDEO.mezzo.fun.Viral.Video.Tutorial.Official | Official-mezzo-fun-18-Viral-videos-Clip-XX | 2025-06-22T10:51:28Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T10:50:21Z | <p><a rel="nofollow" title="WATCH NOW" href="https://viralinfo.xyz/video/?v=Katrina+lim+kiffy"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Mezzo Fun Viral Video
Mezzo Fun Full Original Video Goes Viral On Twitter |
New-videos-Sophie-Rain-viral-video-Link/FULL.VIDEO.Sophie.Rain.Spiderman.Viral.Video.Tutorial.Official | New-videos-Sophie-Rain-viral-video-Link | 2025-06-22T10:50:24Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T10:50:04Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
zavq/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_rough_tuna | zavq | 2025-06-22T10:49:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am nocturnal rough tuna",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T17:47:25Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_rough_tuna
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am nocturnal rough tuna
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_rough_tuna
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zavq/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-nocturnal_rough_tuna", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
vomqal/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_frisky_cat | vomqal | 2025-06-22T10:49:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am prowling frisky cat",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T22:43:28Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_frisky_cat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am prowling frisky cat
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_frisky_cat
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vomqal/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-prowling_frisky_cat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rngrye/my-document-classifier | rngrye | 2025-06-22T10:48:53Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-25T16:15:30Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: my-document-classifier
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. -->
# my-document-classifier
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0313
- Accuracy: 0.9910
- F1: 0.9910
## 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: 16
- eval_batch_size: 16
- seed: 22002423
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5447 | 1.0 | 112 | 0.0777 | 0.9821 | 0.9819 |
| 0.0752 | 2.0 | 224 | 0.0958 | 0.9731 | 0.9730 |
| 0.038 | 3.0 | 336 | 0.0711 | 0.9865 | 0.9865 |
| 0.0191 | 4.0 | 448 | 0.0795 | 0.9865 | 0.9865 |
| 0.0066 | 5.0 | 560 | 0.0900 | 0.9865 | 0.9865 |
| 0.0063 | 6.0 | 672 | 0.0945 | 0.9865 | 0.9865 |
| 0.0014 | 7.0 | 784 | 0.1040 | 0.9865 | 0.9865 |
| 0.0011 | 8.0 | 896 | 0.1023 | 0.9865 | 0.9865 |
| 0.001 | 9.0 | 1008 | 0.1027 | 0.9865 | 0.9865 |
| 0.0009 | 10.0 | 1120 | 0.1026 | 0.9865 | 0.9865 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rugged_tall_starfish | haedahae | 2025-06-22T10:47:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rugged tall starfish",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T04:39:31Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rugged_tall_starfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rugged tall starfish
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rugged_tall_starfish
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-rugged_tall_starfish", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fanged_camouflaged_slug | haedahae | 2025-06-22T10:45:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fanged camouflaged slug",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T05:08:10Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fanged_camouflaged_slug
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fanged camouflaged slug
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fanged_camouflaged_slug
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haedahae/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-fanged_camouflaged_slug", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
litagin/anime_speaker_embedding_by_va_ecapa_tdnn_groupnorm | litagin | 2025-06-22T10:45:34Z | 0 | 0 | null | [
"speaker-verification",
"speaker-identification",
"speaker-embedding",
"audio",
"voice",
"speech",
"audio-classification",
"ja",
"dataset:OOPPEENN/VisualNovel_Dataset",
"base_model:speechbrain/spkrec-ecapa-voxceleb",
"base_model:finetune:speechbrain/spkrec-ecapa-voxceleb",
"license:mit",
"region:us"
] | audio-classification | 2025-06-22T08:23:52Z | ---
license: mit
datasets:
- OOPPEENN/VisualNovel_Dataset
language:
- ja
base_model:
- speechbrain/spkrec-ecapa-voxceleb
pipeline_tag: audio-classification
tags:
- speaker-verification
- speaker-identification
- speaker-embedding
- audio
- voice
- speech
---
# Anime Speaker Embedding
See [GitHub](https://github.com/litagin02/anime_speaker_embedding) repo.
Speaker embedding model for suitable for anime domain.
[English README](#English-README)
アニメドメインに適した話者埋め込みモデル。
## 概要
- [SpeechBrain](https://github.com/speechbrain/speechbrain) の ECAPA-TDNN モデルを、[OOPPEENN/56697375616C4E6F76656C5F44617461736574](https://huggingface.co/datasets/OOPPEENN/56697375616C4E6F76656C5F44617461736574) で学習
- アニメおよびビジュアルノベルの文脈での話者埋め込みタスク向けに設計
- **2025-06-22: Voice Actor（VA）バリアントを追加**（バージョン0.2.0）。デフォルトのモデルよりも同一キャラがまとまりやすくなるモデル（比較は表参照）
## 特長
- 日本語アニメ調の演技音声や非言語発話に特化
- 他の通常の話者埋め込みモデルではまったく区別できない、日本のノベルゲーの文化の中で非常に重要なNSFWな性的発声（喘ぎ・チュパ音など）にも対応
## モデルバリアント
- **char**（デフォルト）: キャラクターを推定するようにトレーニングされたモデル。声優ではなくキャラクターを区別（同じ声優が演じる別キャラクターも別話者として学習）
- **va**（バージョン0.2.0で追加）: 声優を推定するようにトレーニングされたモデル。キャラクターのスタイル差よりも声優ごとの一貫性を重視
同一キャラクターに対して、charモデルは埋め込みの分散が大きく、vaモデルは分散が小さくなる傾向があります。例えば下記のGame1での違いを見ると、charモデルは細かく同一話者でも分離されているのに対し、vaモデルは同一話者の埋め込みが近くに集まっています。
## 注意
- 話者を積極的に区別しようとする性質のため、同一話者の埋め込み間のコサイン類似度は他モデルより低めです
## インストール
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu128 # GPU利用時
pip install anime_speaker_embedding
```
## 使い方
```python
from anime_speaker_embedding import AnimeSpeakerEmbedding
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AnimeSpeakerEmbedding(device=device, variant="char") # variant="va" でVAモデル
audio_path = "path/to/audio.wav"
embedding = model.get_embedding(audio_path)
print(embedding.shape) # (192,) の np.ndarray
```
使用例や可視化例は [example.ipynb](example.ipynb) を参照してください。
## 他モデルとの比較
トレーニングセットに含まれないゲーム音声の埋め込みの様子:
| モデル | Game1 | Game2 | Game3 | Game4 |
|-------|-------|-------|-------|-------|
| [**⭐ VA model**](https://huggingface.co/litagin/anime_speaker_embedding_by_va_ecapa_tdnn_groupnorm) |  |  |  |  |
| [**⭐ Char model**](https://huggingface.co/litagin/anime_speaker_embedding_ecapa_tdnn_groupnorm) |  |  |  |  |
| [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) |  |  |  |  |
| [pyannote/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) |  |  |  |  |
| [Resemblyzer](https://github.com/resemble-ai/Resemblyzer) |  |  |  |  |
- Game1とGame2はNSFW音声を含み、Game3とGame4は含まない
- Game4では茶色と黄色の話者は実際には同一キャラクター
## モデル詳細
### モデルアーキテクチャ
本モデルはSpeechBrainのECAPA-TDNN全てのBatchNormレイヤーをGroupNormに置き換えています。元のBatchNorm層で評価時におそらく統計のドリフトが発生し、うまく推論できなかったためです。
#### データセット
##### `char`バリアント
[OOPPEENN/56697375616C4E6F76656C5F44617461736574](https://huggingface.co/datasets/OOPPEENN/56697375616C4E6F76656C5F44617461736574)の全音声ファイルから破損ファイル等を除外し、100ファイル未満の話者を除外。最終データセット:
- train: 6,260,482 ファイル、valid: 699,488 ファイル、合計 6,959,970 ファイル
- 7,357 人のキャラクター
##### `va`バリアント
[litagin/VisualNovel_Dataset_Metadata](https://huggingface.co/datasets/litagin/VisualNovel_Dataset_Metadata)を用いて、VNDBに声優が登録されているキャラクターのみ使用。最終データセット:
- train: 6,603,080 ファイル、valid: 348,034 ファイル、合計 6,951,114 ファイル
- 989 人の声優
### 学習プロセス
#### `char`バリアント
- [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb)をベースモデルとして使用
- その後BatchNormをすべてGroupNormに置換
- fbank前に `x = x * 32768.0` のスケーリングを追加（ChatGPTがそういうコード出してきたので……。あとからこのスケーリングは互換性上よくないことに気づいたけど手遅れでした）
- いろいろ変えてるので、実際はファインチューニングではなくスクラッチからの学習に近いと思います
- ファイル数が多い上位100、1000キャラクターのサブセットで事前学習
- フルデータセットで学習
- オンラインデータ拡張（リバーブ、バックグラウンドノイズ、各種フィルタ等）を加えて再学習
- 同一シリーズ・同一キャラクター名で混同行列が高いキャラクター（同じゲームシリーズの同一キャラ相当）をいくつかマージして学習
#### `va`バリアント
- Charバリアントの埋め込み器をベースに、データセットを声優のに変えてファインチューニング
- オーグメンテーション確率0.8
- バリデーションセットでMacro精度・再現率・F1・EERを評価し、EER0.41%のモデルを採用 (Macro precision 95.97%, Recall 97.83%、F1 96.80%)
**トレーニングコードは別リポジトリで公開予定です。**
# English README
## Overview
- ECAPA-TDNN model (from [SpeechBrain](https://github.com/speechbrain/speechbrain)) trained on [OOPPEENN/56697375616C4E6F76656C5F44617461736574](https://huggingface.co/datasets/OOPPEENN/56697375616C4E6F76656C5F44617461736574)
- This model is designed for speaker embedding tasks in anime and visual novel contexts.
- **2025-06-22: Added Voice Actor (VA) variant** in version 0.2.0, which is less eager to distinguish speakers compared to the default Character (char) variant.
## Features
- Well-suited for **Japanese anime-like** voices, including **non-verbal vocalizations** or **acted voices**
- Also works well for *NSFW erotic utterances and vocalizations* such as aegi (喘ぎ) and chupa-sound (チュパ音), which are important in Japanese Visual Novel games, while other usual speaker embedding models cannot distinguish such voices of different speakers at all!
## Model Variants
- **char** (default): Trained to guess character voices, not voice actors; eager to distinguish speakers (even two characters with the same voice actor).
- **va** (added in ver 0.2.0): Trained on voice actors, not characters; less eager to distinguish speakers.
For a single fixed character, the **char** model produces embeddings with higher variance by style, while the **va** model keeps embeddings more similar (lower variance).
## Note
- Because this model tries to eagerly distinguish speakers, cosine similarity values between embeddings of the same speaker are usually lower than in other embedding models.
## Installation
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu128 # if you want to use GPU
pip install anime_speaker_embedding
```
## Usage
```python
from anime_speaker_embedding import AnimeSpeakerEmbedding
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AnimeSpeakerEmbedding(device=device, variant="char") # or variant="va" for Voice Actor model
audio_path = "path/to/audio.wav"
embedding = model.get_embedding(audio_path)
print(embedding.shape) # np.ndarray with shape (192,)
```
See [example.ipynb](example.ipynb) for usage and visualization examples.
## Comparison with other models
t-SNE plots of embeddings from some Galgames (not included in the training set!):
| Model | Game1 | Game2 | Game3 | Game4 |
|-------|-------|-------|-------|-------|
| [**⭐ VA model**](https://huggingface.co/litagin/anime_speaker_embedding_by_va_ecapa_tdnn_groupnorm) |  |  |  |  |
| [**⭐ Char model**](https://huggingface.co/litagin/anime_speaker_embedding_ecapa_tdnn_groupnorm) |  |  |  |  |
| [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb) |  |  |  |  |
| [pyannote/wespeaker-voxceleb-resnet34-LM](https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM) |  |  |  |  |
| [Resemblyzer](https://github.com/resemble-ai/Resemblyzer) |  |  |  |  |
- Game1 and Game2 contain NSFW voices; Game3 and Game4 do not.
- In Game4, the brown and yellow speakers are actually the same character.
## Model Details
### Model Architecture
The actual model is SpeechBrain’s ECAPA-TDNN with all BatchNorm layers replaced by GroupNorm, due to statistical drift issues during evaluation.
#### Dataset
##### Char variant
From the [OOPPEENN/56697375616C4E6F76656C5F44617461736574](https://huggingface.co/datasets/OOPPEENN/56697375616C4E6F76656C5F44617461736574) dataset, broken files and speakers with fewer than 100 files were excluded. Final:
- train: 6,260,482 files, valid: 699,488 files, total: 6,959,970 files
- 7,357 speakers
##### VA variant
Using [litagin/VisualNovel_Dataset_Metadata](https://huggingface.co/datasets/litagin/VisualNovel_Dataset_Metadata), only characters whose VAs are in VNDB were kept. Final:
- train: 6,603,080 files, valid: 348,034 files, total: 6,951,114 files
- 989 speakers
### Training process
#### `char` variant
- Base: [speechbrain/spkrec-ecapa-voxceleb](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb); replaced BN→GN; added `x = x * 32768.0` before fbank
- ChatGPT suggested this scaling, but it turned out to be incompatible later.
- I guess the model is rather trained from scratch, not fine-tuned actually.
- Pretrained on top-100/1000 speakers subset
- Trained on full dataset
- Retrained with online augmentations (reverb, noise, filters)
- Merged speakers with high confusion from same series/character
#### `va` variant
- Fine-tuned `char` backbone (aug prob 0.8)
- Selected model with best EER (0.41%); Macro precision 95.97%, recall 97.83%, F1 96.80%
**Training code to be released separately.**
|
harshasurampudi/upsc-classifier-gemma1b | harshasurampudi | 2025-06-22T10:43:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-22T10:43:39Z | ---
base_model: upsc-article-classifier-fp16
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** harshasurampudi
- **License:** apache-2.0
- **Finetuned from model :** upsc-article-classifier-fp16
This gemma3_text 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)
|
Skryaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-gilded_endangered_bat | Skryaga | 2025-06-22T10:43:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am gilded endangered bat",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T03:04:16Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-gilded_endangered_bat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am gilded endangered bat
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-gilded_endangered_bat
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Skryaga/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-gilded_endangered_bat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dhanraj2006/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_trotting_beaver | dhanraj2006 | 2025-06-22T10:41:55Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ferocious trotting beaver",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-15T21:09:37Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_trotting_beaver
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am ferocious trotting beaver
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_trotting_beaver
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="dhanraj2006/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_trotting_beaver", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
efd2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug | efd2 | 2025-06-22T10:41:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am foxy gentle slug",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T03:43:26Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am foxy gentle slug
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="efd2/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-foxy_gentle_slug", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nebula65/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_quick_pigeon | Nebula65 | 2025-06-22T10:39:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slender quick pigeon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T17:47:11Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_quick_pigeon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slender quick pigeon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_quick_pigeon
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Nebula65/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_quick_pigeon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
lokas/spam-emails-classifier | lokas | 2025-06-22T10:38:24Z | 0 | 0 | keras | [
"keras",
"tensorboard",
"lstm",
"spam-classification",
"text-classification",
"binary-classification",
"email",
"deep-learning",
"en",
"dataset:SetFit/enron_spam",
"doi:10.57967/hf/5845",
"license:mit",
"region:us"
] | text-classification | 2025-06-22T07:42:26Z | ---
language: en
license: mit
tags:
- keras
- lstm
- spam-classification
- text-classification
- binary-classification
- email
- deep-learning
library_name: keras
pipeline_tag: text-classification
model_name: Spam Email Classifier (BiLSTM)
datasets:
- SetFit/enron_spam
---
# 📧 Spam Email Classifier using BiLSTM
This model uses a **Bidirectional LSTM (BiLSTM)** architecture built with **Keras** to classify email messages as **Spam** or **Ham**. It was trained on the [Enron Spam Dataset](https://huggingface.co/datasets/SetFit/enron_spam) using GloVe word embeddings.
---
## 🧠 Model Architecture
- **Tokenizer**: Keras `Tokenizer` trained on the Enron dataset
- **Embedding**: Pretrained [GloVe.6B.100d](https://nlp.stanford.edu/projects/glove/)
- **Model**: `Embedding → BiLSTM → Dropout → Dense(sigmoid)`
- **Input**: English email/message text
- **Output**: `0 = Ham`, `1 = Spam`
---
## 🧪 Example Usage
```python
from tensorflow.keras.models import load_model
from huggingface_hub import hf_hub_download
import pickle
from tensorflow.keras.preprocessing.sequence import pad_sequences
# Load files from HF Hub
model_path = hf_hub_download("lokas/spam-emails-classifier", "model.h5")
tokenizer_path = hf_hub_download("lokas/spam-emails-classifier", "tokenizer.pkl")
# Load model and tokenizer
model = load_model(model_path)
with open(tokenizer_path, "rb") as f:
tokenizer = pickle.load(f)
# Prediction function
def predict_spam(text):
seq = tokenizer.texts_to_sequences([text])
padded = pad_sequences(seq, maxlen=50) # must match training maxlen
pred = model.predict(padded)[0][0]
return "🚫 Spam" if pred > 0.5 else "✅ Not Spam"
# Example
print(predict_spam("Win a free iPhone now!"))
|
mpratohernandez/maru-centeia | mpratohernandez | 2025-06-22T10:37:47Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-22T10:16:28Z | ---
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: MARU-CENTEIA
---
# Maru Centeia
<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 `MARU-CENTEIA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MARU-CENTEIA",
"lora_weights": "https://huggingface.co/mpratohernandez/maru-centeia/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('mpratohernandez/maru-centeia', weight_name='lora.safetensors')
image = pipeline('MARU-CENTEIA').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: 1250
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/mpratohernandez/maru-centeia/discussions) to add images that show off what you’ve made with this LoRA.
|
Nuclearvision/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stalking_beaked_grasshopper | Nuclearvision | 2025-06-22T10:36:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stalking beaked grasshopper",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T00:13:29Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stalking_beaked_grasshopper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stalking beaked grasshopper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stalking_beaked_grasshopper
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Nuclearvision/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stalking_beaked_grasshopper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dgrzyska/gemma-2b-grammar-mlc-q4f32-1 | dgrzyska | 2025-06-22T10:36:25Z | 0 | 0 | null | [
"grammar",
"gec",
"en",
"base_model:google/gemma-2-2b-it",
"base_model:finetune:google/gemma-2-2b-it",
"license:gemma",
"region:us"
] | null | 2025-06-22T09:18:40Z | ---
license: gemma
language:
- en
base_model:
- google/gemma-2-2b-it
tags:
- grammar
- gec
---
## License
This model is a fine-tuned version of [Google’s Gemma 2B model](https://ai.google.dev/gemma), and is distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
By using this model, you agree to comply with those terms. This fine-tuned version is not an official release from Google.
|
EXCLUSIVE-TRENDING-Katrina-Lim-Viral-Video/FULL.VIDEO.Katrina.Lim.Viral.Video.Tutorial.Official | EXCLUSIVE-TRENDING-Katrina-Lim-Viral-Video | 2025-06-22T10:35:46Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T10:35:23Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
raviadi123/gemma-3-finetune | raviadi123 | 2025-06-22T10:35:23Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-22T10:35:14Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** raviadi123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
MCTS-Refine/MCTS-Refine-7B | MCTS-Refine | 2025-06-22T10:35:18Z | 0 | 0 | null | [
"safetensors",
"qwen2",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:mit",
"region:us"
] | null | 2025-06-07T10:42:13Z | ---
license: mit
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
--- |
NewGithubAI/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stinky_beaked_skunk | NewGithubAI | 2025-06-22T10:34:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stinky beaked skunk",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T10:47:10Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stinky_beaked_skunk
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stinky beaked skunk
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stinky_beaked_skunk
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="NewGithubAI/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-stinky_beaked_skunk", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
franciscommr10/mistral-lora-finetuned-cyclist-nutrition_1000 | franciscommr10 | 2025-06-22T10:33:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:33:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
LittleRedRidingHood73/DepressionEmotionModels | LittleRedRidingHood73 | 2025-06-22T10:32:57Z | 0 | 0 | null | [
"joblib",
"license:apache-2.0",
"region:us"
] | null | 2025-06-22T10:27:41Z | ---
license: apache-2.0
---
|
QinShiHuangisavailable/model1 | QinShiHuangisavailable | 2025-06-22T10:32:29Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:deepseek-ai/deepseek-math-7b-rl",
"base_model:finetune:deepseek-ai/deepseek-math-7b-rl",
"endpoints_compatible",
"region:us"
] | null | 2025-06-21T21:54:02Z | ---
base_model: deepseek-ai/deepseek-math-7b-rl
library_name: transformers
model_name: model1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for model1
This model is a fine-tuned version of [deepseek-ai/deepseek-math-7b-rl](https://huggingface.co/deepseek-ai/deepseek-math-7b-rl).
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="QinShiHuangisavailable/model1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
apriasmoro/858325b3-3f7f-4cca-8e72-c9c7f57952a4 | apriasmoro | 2025-06-22T10:30:56Z | 15 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:adapter:Qwen/Qwen3-8B-Base",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-28T16:34:54Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-8B-Base
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 858325b3-3f7f-4cca-8e72-c9c7f57952a4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: Qwen/Qwen3-8B-Base
bf16: true
chat_template: llama3
datasets:
- data_files:
- 45c346a7c1e52747_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: None
field_instruction: instruct
field_output: output
field_system: None
format: None
no_input_format: None
system_format: '{system}'
system_prompt: None
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/858325b3-3f7f-4cca-8e72-c9c7f57952a4
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1
micro_batch_size: 4
mlflow_experiment_name: /tmp/45c346a7c1e52747_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 380
sequence_len: 2048
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c732d2b4-46df-4ed8-83ee-7525f648965h
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: c732d2b4-46df-4ed8-83ee-7525f648965h
warmup_steps: 100
weight_decay: 0.01
```
</details><br>
# 858325b3-3f7f-4cca-8e72-c9c7f57952a4
This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0031 | 1 | 1.3931 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
hazentr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo | hazentr | 2025-06-22T10:30:20Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am roaring colorful buffalo",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-03T12:28:07Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am roaring colorful buffalo
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo
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="hazentr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_colorful_buffalo", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
EXCLUSIVE-TRENDING-Trishakar-Madhu-Videos/FULL.VIDEO.Trishakar.Madhu.Viral.Video.Tutorial.Official | EXCLUSIVE-TRENDING-Trishakar-Madhu-Videos | 2025-06-22T10:28:28Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T10:27:53Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
hazentr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grunting_koala | hazentr | 2025-06-22T10:26:32Z | 26 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slender grunting koala",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-02T16:33:05Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grunting_koala
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slender grunting koala
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grunting_koala
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="hazentr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-slender_grunting_koala", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dhanraj2006/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wise_yawning_heron | dhanraj2006 | 2025-06-22T10:25:57Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am wise yawning heron",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-19T18:47:02Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wise_yawning_heron
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am wise yawning heron
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wise_yawning_heron
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="dhanraj2006/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wise_yawning_heron", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AXERA-TECH/YOLOv7-Face | AXERA-TECH | 2025-06-22T10:23:56Z | 0 | 0 | null | [
"onnx",
"YOLOv7",
"YOLOv7-Face",
"object-detection",
"en",
"license:mit",
"region:us"
] | object-detection | 2025-03-23T07:44:22Z | ---
license: mit
language:
- en
pipeline_tag: object-detection
tags:
- YOLOv7
- YOLOv7-Face
---
# YOLOv7-FACE
This version of YOLOv7-FACE has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 3.4
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through
- [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide
- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html)
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
- AX630C
- [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html)
- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM)
- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit)
|Chips|cost|
|--|--|
|AX650| 12.6 ms |
|AX630C| TBD ms |
## How to use
Download all files from this repository to the device
```
root@ax650:~/YOLOv7-Face# tree
.
|-- ax650
| `-- yolov7-face.axmodel
|-- ax_yolov7_face
|-- selfie.jpg
`-- yolov7_face_out.jpg
```
### Inference
Input image:
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
```
root@ax650:~/YOLOv7-Face# ./ax_yolov7_face -m ax650/yolov7-face.axmodel -i selfie.jpg
--------------------------------------
model file : ax650/yolov7-face.axmodel
image file : selfie.jpg
img_h, img_w : 640 640
--------------------------------------
Engine creating handle is done.
Engine creating context is done.
Engine get io info is done.
Engine alloc io is done.
Engine push input is done.
--------------------------------------
post process cost time:8.70 ms
--------------------------------------
Repeat 1 times, avg time 12.59 ms, max_time 12.59 ms, min_time 12.59 ms
--------------------------------------
detection num: 174
0: 91%, [1137, 869, 1283, 1065], face
0: 91%, [1424, 753, 1570, 949], face
......
0: 45%, [1658, 362, 1677, 387], face
0: 45%, [1445, 437, 1467, 462], face
--------------------------------------
root@ax650:~/YOLOv7-Face#
```
Output image:
 |
Sohan2004/ai4b-Fintuned | Sohan2004 | 2025-06-22T10:21:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"IndicTrans",
"text2text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text2text-generation | 2025-06-22T10:18:06Z | ---
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]
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AlinaTsai/BREEZE_ecophs_28_20250622_NEW | AlinaTsai | 2025-06-22T10:21:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:21:00Z | ---
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.
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## How to Get Started with the Model
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## Training Details
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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wendell0218/Janus-FocusDiff-7B | wendell0218 | 2025-06-22T10:21:10Z | 6 | 0 | null | [
"safetensors",
"multi_modality",
"arxiv:2506.05501",
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T15:57:17Z | ---
license: apache-2.0
---
<h2 align="center" style="line-height: 25px;">
FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL
</h2>
<p align="center">
<a href="https://arxiv.org/abs/2506.05501" style="display: inline-block; margin: 0 5px;">
<img src="https://img.shields.io/badge/Paper-red?style=flat&logo=arxiv" style="height: 15px;">
</a>
<a href="https://focusdiff.github.io/" style="display: inline-block; margin: 0 5px;">
<img src="https://img.shields.io/badge/Project Page-white?style=flat&logo=google-docs" style="height: 15px;">
</a>
<a href="https://github.com/wendell0218/FocusDiff" style="display: inline-block; margin: 0 5px;">
<img src="https://img.shields.io/badge/Code-black?style=flat&logo=github" style="height: 15px;">
</a>
<a href="https://huggingface.co/wendell0218/Janus-FocusDiff-7B" style="display: inline-block; margin: 0 5px;">
<img src="https://img.shields.io/badge/-%F0%9F%A4%97%20Checkpoint-orange?style=flat" style="height: 15px;"/>
</a>
</p>
<div align="center">
<span style="font-size: smaller;">
Kaihang Pan<sup>1*</sup>, Wendong Bu<sup>1*</sup>, Yuruo Wu<sup>1*</sup>, Yang Wu<sup>2</sup>, Kai Shen<sup>1</sup>, Yunfei Li<sup>2</sup>,
<br>Hang Zhao<sup>2</sup>, Juncheng Li<sup>1†</sup>, Siliang Tang<sup>1</sup>, Yueting Zhuang<sup>1</sup>
<br><sup>1</sup>Zhejiang University, <sup>2</sup>Ant Group
<br>*Equal Contribution, <sup>†</sup>Corresponding Authors
</span>
</div>
## 🚀 Overview
**FocusDiff** is a new method for improving fine-grained text-image alignment in autoregressive text-to-image models. By introducing the **FocusDiff-Data** dataset and a novel **Pair-GRPO** reinforcement learning framework, we help models learn subtle semantic differences between similar text-image pairs. Based on paired data in FocusDiff-Data, we further introduce the **PairComp** Benchmark, which focuses on subtle semantic differences.
Key Contributions:
1. **PairComp Benchmark**: A new benchmark focusing on fine-grained differences in text prompts.
<img src="https://raw.githubusercontent.com/wendell0218/FocusDiff/refs/heads/main/assets/benchmark.png" width="100%"/>
2. **FocusDiff Approach**: A method using paired data and reinforcement learning to enhance fine-grained text-image alignment.
<div style="text-align: center;">
<img src="https://raw.githubusercontent.com/wendell0218/FocusDiff/refs/heads/main/assets/grpo.png" width="100%" />
</div>
3. **SOTA Results**: Our model is evaluated with the top performance on multiple benchmarks including **GenEval**, **T2I-CompBench**, **DPG-Bench**, and our newly proposed **PairComp** benchmark.
## ✨️ Quickstart
**1. Prepare Environment**
We recommend using Python 3.10 and setting up a virtual environment:
```bash
# clone our repo
git clone https://github.com/wendell0218/FocusDiff.git
cd FocusDiff
# prepare python environment
conda create -n focus-diff python=3.10
conda activate focus-diff
pip install -r requirements.txt
```
**2. Prepare Pretrained Model**
FocusDiff utilizes `Janus-Pro-7B` as the pretrained model for subsequent supervised fine-tuning. You can download the corresponding model using the following command:
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/deepseek-ai/Janus-Pro-7B
cd Janus-Pro-7B
git lfs pull
```
**3. Start Generating!**
```python
import os
import torch
import PIL.Image
import numpy as np
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
@torch.inference_mode()
def generate(
mmgpt: MultiModalityCausalLM,
vl_chat_processor: VLChatProcessor,
prompt: str,
temperature: float = 1.0,
parallel_size: int = 4,
cfg_weight: float = 5.0,
image_token_num_per_image: int = 576,
img_size: int = 384,
patch_size: int = 16,
img_top_k: int = 1,
img_top_p: float = 1.0,
):
images = []
input_ids = vl_chat_processor.tokenizer.encode(prompt)
input_ids = torch.LongTensor(input_ids)
tokens = torch.zeros((parallel_size*2, len(input_ids)), dtype=torch.int).cuda()
for i in range(parallel_size*2):
tokens[i, :] = input_ids
if i % 2 != 0:
tokens[i, 1:-1] = vl_chat_processor.pad_id
inputs_embeds = mmgpt.language_model.get_input_embeddings()(tokens)
generated_tokens = torch.zeros((parallel_size, image_token_num_per_image), dtype=torch.int).cuda()
for i in range(image_token_num_per_image):
outputs = mmgpt.language_model.model(inputs_embeds=inputs_embeds, use_cache=True, past_key_values=outputs.past_key_values if i != 0 else None)
hidden_states = outputs.last_hidden_state
logits = mmgpt.gen_head(hidden_states[:, -1, :])
logit_cond = logits[0::2, :]
logit_uncond = logits[1::2, :]
logits = logit_uncond + cfg_weight * (logit_cond-logit_uncond)
if img_top_k:
v, _ = torch.topk(logits, min(img_top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float("-inf")
probs = torch.softmax(logits / temperature, dim=-1)
if img_top_p:
probs_sort, probs_idx = torch.sort(probs,
dim=-1,
descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > img_top_p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
else:
next_token = torch.multinomial(probs, num_samples=1)
generated_tokens[:, i] = next_token.squeeze(dim=-1)
next_token = torch.cat([next_token.unsqueeze(dim=1), next_token.unsqueeze(dim=1)], dim=1).view(-1)
img_embeds = mmgpt.prepare_gen_img_embeds(next_token)
inputs_embeds = img_embeds.unsqueeze(dim=1)
dec = mmgpt.gen_vision_model.decode_code(generated_tokens.to(dtype=torch.int), shape=[parallel_size, 8, img_size//patch_size, img_size//patch_size])
dec = dec.to(torch.float32).cpu().numpy().transpose(0, 2, 3, 1)
dec = np.clip((dec + 1) / 2 * 255, 0, 255)
visual_img = np.zeros((parallel_size, img_size, img_size, 3), dtype=np.uint8)
visual_img[:, :, :] = dec
for i in range(parallel_size):
images.append(PIL.Image.fromarray(visual_img[i]))
return images
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str, default="deepseek-ai/Janus-Pro-7B")
parser.add_argument("--ckpt_path", type=str, default=None)
parser.add_argument("--caption", type=str, default="a brown giraffe and a white stop sign")
parser.add_argument("--gen_path", type=str, default='results/samples')
parser.add_argument("--cfg", type=float, default=5.0)
parser.add_argument("--parallel_size", type=int, default=4)
args = parser.parse_args()
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(args.model_path)
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(args.model_path, trust_remote_code=True)
if args.ckpt_path is not None:
state_dict = torch.load(f"{args.ckpt_path}", map_location="cpu")
vl_gpt.load_state_dict(state_dict)
vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()
prompt = f'<|User|>: {args.caption}\n\n<|Assistant|>:<begin_of_image>'
images = generate(
vl_gpt,
vl_chat_processor,
prompt,
parallel_size = args.parallel_size,
cfg_weight = args.cfg,
)
if not os.path.exists(args.gen_path):
os.makedirs(args.gen_path, exist_ok=True)
for i in range(args.parallel_size):
img_name = str(i).zfill(4)+".png"
save_path = os.path.join(args.gen_path, img_name)
images[i].save(save_path)
```
## 🤝 Acknowledgment
Our project is developed based on the following repositories:
- [Janus-Series](https://github.com/deepseek-ai/Janus): Unified Multimodal Understanding and Generation Models
- [Open-R1](https://github.com/huggingface/open-r1): Fully open reproduction of DeepSeek-R1
## 📜 Citation
If you find this work useful for your research, please cite our paper and star our git repo:
```bibtex
@article{pan2025focusdiff,
title={FocusDiff: Advancing Fine-Grained Text-Image Alignment for Autoregressive Visual Generation through RL},
author={Pan, Kaihang and Bu, Wendong and Wu, Yuruo and Wu, Yang and Shen, Kai and Li, Yunfei and Zhao, Hang and Li, Juncheng and Tang, Siliang and Zhuang, Yueting},
journal={arXiv preprint arXiv:2506.05501},
year={2025}
}
``` |
JeloH/qwen-textgen-modelV_M2_SRC_Ass | JeloH | 2025-06-22T10:18:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-19T19:01:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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saish-shetty/SmolLM2-1.7B-pre-trained | saish-shetty | 2025-06-22T10:17:24Z | 0 | 0 | null | [
"safetensors",
"llama",
"text-generation",
"causal-lm",
"accelerate",
"smollm2",
"cosmopedia",
"pre-trained",
"en",
"base_model:HuggingFaceTB/SmolLM2-1.7B",
"base_model:finetune:HuggingFaceTB/SmolLM2-1.7B",
"license:mit",
"region:us"
] | text-generation | 2025-06-22T08:55:44Z | ---
license: mit
base_model: HuggingFaceTB/SmolLM2-1.7B
tags:
- text-generation
- causal-lm
- accelerate
- smollm2
- cosmopedia
- pre-trained
language:
- en
pipeline_tag: text-generation
---
# SmolLM2-1.7B pre-trained on Cosmopedia-v2
This model is a pre-trained version of [HuggingFaceTB/SmolLM2-1.7B](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B) on the Cosmopedia-v2 dataset.
## Model Details
- **Base Model**: HuggingFaceTB/SmolLM2-1.7B (1.7B parameters)
- **pre-trained on**: Cosmopedia-v2 dataset (1B tokens)
- **Training Steps**: 30,000
- **Final Loss**: 3.7547
- **Training Date**: 2025-06-21
## Training Configuration
```python
- Batch Size per Device: 1
- Gradient Accumulation Steps: 16
- Learning Rate: 2e-5
- Sequence Length: 2048
- Optimizer: 8-bit AdamW
- Mixed Precision: bf16
```
## Dataset
The model was trained on Cosmopedia-v2, a high-quality synthetic dataset containing educational content across various topics.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("saish-shetty/SmolLM2-1.7B-pre-trained")
model = AutoModelForCausalLM.from_pretrained(
"saish-shetty/SmolLM2-1.7B-pre-trained",
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Generate text
prompt = "Machine learning is a field of"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
**inputs,
max_new_tokens=50,
temperature=0.7,
top_p=0.9,
do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Performance
The model shows 99% reduction in perplexity in text generation tasks compared to randomised base model weights, with better coherence and domain knowledge from the Cosmopedia-v2 training.
## Training Infrastructure
- **GPUs**: 4x NVIDIA L4 (24GB each)
- **Framework**: Transformers + DeepSpeed ZeRO Stage 2
- **Distributed Training**: Accelerate
- **Memory Optimization**: 8-bit optimizer, gradient checkpointing
## Limitations
- The model inherits limitations from the base SmolLM2-1.7B model
- Training was focused on educational content from Cosmopedia-v2
- May not perform optimally on tasks outside the training domain
## Citation
If you use this model, please cite:
```bibtex
@misc{smollm2-cosmopedia-finetune,
title={SmolLM2-1.7B pre-trained on Cosmopedia-v2},
author={Saish Shetty},
year={2025},
url={https://huggingface.co/saish-shetty/SmolLM2-1.7B-pre-trained}
}
```
## License
This model is released under the MIT license, following the base model's licensing.
|
BullUp/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_nimble_snake | BullUp | 2025-06-22T10:15:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am robust nimble snake",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-16T01:44:53Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_nimble_snake
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am robust nimble snake
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_nimble_snake
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="BullUp/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-robust_nimble_snake", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hulking_sharp_rhino | SIGTIR | 2025-06-22T10:13:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hulking sharp rhino",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T03:16:15Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hulking_sharp_rhino
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hulking sharp rhino
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hulking_sharp_rhino
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SIGTIR/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-hulking_sharp_rhino", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
miladalsh/qwen-trained-researcher-on-deepseek | miladalsh | 2025-06-22T10:13:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T17:08:30Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: qwen-trained-researcher-on-deepseek
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen-trained-researcher-on-deepseek
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="miladalsh/qwen-trained-researcher-on-deepseek", 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/milad-it/training-llama-on-conversations/runs/9to3chx0)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mastur96/f5359d4b-4455-4537-bbdc-a1593cb528b0 | mastur96 | 2025-06-22T10:13:19Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-21T07:17:09Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
cyberdelia/CyberRealisticPony | cyberdelia | 2025-06-22T10:13:00Z | 11,101 | 64 | diffusers | [
"diffusers",
"stable-diffusion",
"sdxl",
"text-to-image",
"photorealistic",
"cyberrealistic",
"pony",
"image-generation",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-05-09T10:12:22Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- sdxl
- text-to-image
- photorealistic
- cyberrealistic
- pony
- image-generation
- diffusers
model-index:
- name: CyberRealistic Pony
results: []
---
# CyberRealistic Pony
**CyberRealistic Pony** is the awesome Pony Diffusion with some CyberRealistic elements.
---
## ✨ Features
- **Photorealism**: Generates highly detailed and realistic pony images, capturing intricate textures and lighting.
- **Ease of Use**: Achieves impressive results with straightforward prompts.
- **Integrated VAE**: Comes with a baked-in Variational Autoencoder for enhanced image quality.
- **Versatility**: Suitable for various applications, including character design, illustrations, and concept art.
---
## 🛠️ Recommended Settings
| Parameter | Recommended Value |
|-----------------|------------------------------------------------|
| Sampling Steps | 30+ |
| Sampler | DPM++ SDE Karras / DPM++ 2M Karras / Euler a |
| Resolution | 896x1152 / 832x1216 |
| CFG Scale | 5 |
| VAE | Already baked-in |
---
## 🧾 Example Prompts
> score_9, score_8_up, score_7_up, (SUBJECT),
---
## 📸 Example Outputs
---
## 🔗 Links
- [Civitai Model Page](https://civitai.com/models/443821/cyberrealistic-pony)
---
## 🚫 Limitations
- May produce content that could be considered sensitive; use responsibly.
- Some prompts involving abstract or non-pony content may not perform as well due to the model's specialized training.
- Lighting and textures may occasionally be too clean or smooth depending on sampling choices.
---
## ✅ License
This model is distributed under the **CreativeML Open RAIL++-M License**, which allows commercial and non-commercial use, with proper credit and no malicious usage.
> [License details](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
Longyka/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_long_wallaby | Longyka | 2025-06-22T10:12:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am bipedal long wallaby",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T00:00:04Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_long_wallaby
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am bipedal long wallaby
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_long_wallaby
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Longyka/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-bipedal_long_wallaby", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AXERA-TECH/YOLO11-Pose | AXERA-TECH | 2025-06-22T10:11:27Z | 29 | 0 | null | [
"onnx",
"Ultralytics",
"YOLO11",
"YOLO11-POSE",
"object-detection",
"en",
"base_model:Ultralytics/YOLO11",
"base_model:quantized:Ultralytics/YOLO11",
"license:mit",
"region:us"
] | object-detection | 2025-03-23T07:42:32Z | ---
license: mit
language:
- en
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- Ultralytics
- YOLO11
- YOLO11-POSE
---
# YOLO11-POSE
This version of YOLO11-POSE has been converted to run on the Axera NPU using **w8a16** quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 3.4
## Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through
- [The repo of AXera Platform](https://github.com/AXERA-TECH/ax-samples), which you can get the detial of guide
- [Pulsar2 Link, How to Convert ONNX to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/pulsar2/introduction.html)
## Support Platform
- AX650
- [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
- AX630C
- [爱芯派2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html)
- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM)
- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit)
|Chips|cost|
|--|--|
|AX650| 25 ms |
|AX630C| TBD ms |
## How to use
Download all files from this repository to the device
```
root@ax650:~/YOLO11-Pose# tree
.
|-- ax650
| `-- yolo11x-pose.axmodel
|-- ax_yolo11_pose
|-- football.jpg
`-- yolo11_pose_out.jpg
```
### Inference
Input image:
#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro)
```
root@ax650:~/YOLO11-Pose# ./ax_yolo11_pose -m ax650/yolo11x-pose.axmodel -i football.jpg
--------------------------------------
model file : ax650/yolo11x-pose.axmodel
image file : football.jpg
img_h, img_w : 640 640
--------------------------------------
Engine creating handle is done.
Engine creating context is done.
Engine get io info is done.
Engine alloc io is done.
Engine push input is done.
--------------------------------------
post process cost time:1.40 ms
--------------------------------------
Repeat 1 times, avg time 25.21 ms, max_time 25.21 ms, min_time 25.21 ms
--------------------------------------
detection num: 6
0: 94%, [1350, 337, 1632, 1036], person
0: 93%, [ 492, 477, 658, 1000], person
0: 92%, [ 756, 219, 1126, 1154], person
0: 91%, [ 0, 354, 314, 1108], person
0: 73%, [ 0, 530, 81, 1017], person
0: 54%, [ 142, 589, 239, 1013], person
--------------------------------------
```
Output image:
 |
Logic411/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_energetic_ape | Logic411 | 2025-06-22T10:11:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am swift energetic ape",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T01:45:23Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_energetic_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am swift energetic ape
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_energetic_ape
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Logic411/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-swift_energetic_ape", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BootesVoid/cmc6o6l1l077hbfifoj9ce7md_cmc7h3iw008zfbfif7kh9d3gt | BootesVoid | 2025-06-22T10:09:30Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-22T10:09:28Z | ---
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: MADI
---
# Cmc6O6L1L077Hbfifoj9Ce7Md_Cmc7H3Iw008Zfbfif7Kh9D3Gt
<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 `MADI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MADI",
"lora_weights": "https://huggingface.co/BootesVoid/cmc6o6l1l077hbfifoj9ce7md_cmc7h3iw008zfbfif7kh9d3gt/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc6o6l1l077hbfifoj9ce7md_cmc7h3iw008zfbfif7kh9d3gt', weight_name='lora.safetensors')
image = pipeline('MADI').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc6o6l1l077hbfifoj9ce7md_cmc7h3iw008zfbfif7kh9d3gt/discussions) to add images that show off what you’ve made with this LoRA.
|
laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K | laion | 2025-06-22T10:09:09Z | 164 | 4 | transformers | [
"transformers",
"pytorch",
"open_clip",
"safetensors",
"mammut",
"feature-extraction",
"clip",
"openMammut",
"datacomp",
"zero-shot-image-classification",
"custom_code",
"dataset:mlfoundations/datacomp_1b",
"arxiv:2506.04598",
"license:apache-2.0",
"region:us"
] | zero-shot-image-classification | 2025-06-03T23:34:30Z | ---
datasets:
- mlfoundations/datacomp_1b
library_name: transformers
license: apache-2.0
pipeline_tag: zero-shot-image-classification
tags:
- clip
- openMammut
- datacomp
library_tag:
- open_clip
- transformers
---
# Model card for openMammut-ViT-L-14-DataComp-1.4B-s12.8B-b180K
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [How To Get Started With the Model](#how-to-get-started-with-the-model)
6. [Acknowledgements](#acknowledgements)
7. [Citation](#citation)
# Model Details
## Model Description
An openMammut ViT-L/14 model (224 resolution), able to perform various image recognition and image captioning tasks. Trained on the [DataComp-1.4B](https://github.com/mlfoundations/datacomp), 12.8B samples in total, using [custom OpenCLIP fork](https://github.com/LAION-AI/open_clip_mammut).
Model training done by Jenia Jitsev on [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) at [Juelich Supercomputing Center](https://www.fz-juelich.de/en/ias/jsc), using automated experiment execution workflow [autoexperiment](https://github.com/SLAMPAI/autoexperiment), implemented by Mehdi Cherti.
Training performed in frame of scaling law model and dataset comparison study published in [arXiv:2506.04598](https://arxiv.org/abs/2506.04598). See also the [research repository](https://github.com/LAION-AI/scaling-laws-for-comparison) and [full thread](https://x.com/JJitsev/status/1931569060438737161).
The model weights are directly usable in [HF transformers](#quickstart-with-hf-transformers) (HF version implemented by Marianna Nezhurina) or by using [custom OpenCLIP fork](#using-openclip-codebase).
Both image recognition (classification, retrieval, etc) and text generation (image captioning) tasks are supported.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/mCNQu13oNcdHasaNo3lST.png" alt="openmammut_release_logo" width="60%"/>
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification and retrieval, and generalization capabilities of language-vision learning in general. We also hope it can be used for interdisciplinary studies of the impact of such model, eg when used as component in VLMs or other multi-modal models.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
Details on the DataComp-1.4B training dataset can be found in [DataComp repository](https://github.com/mlfoundations/datacomp) and the [DataComp NeurIPS Oral paper](https://openreview.net/forum?id=dVaWCDMBof).
## Direct Use
Zero-shot image classification, image and text retrieval, segmentation, image captioning. Other uses are possible when employing the model as component in other systems or fine-tuning it for other downstream tasks.
ATTENTION: currently, if [using openCLIP code base](#using-openclip-codebase), [custom openCLIP fork](https://github.com/LAION-AI/open_clip_mammut) is required to work with the model.
Integrating openMaMMUT code into main [openCLIP repository](https://github.com/mlfoundations/open_clip) is work in progress. Any volunteers helping with intergration highly welcome, join [LAION discord](https://discord.gg/BZqhreFazY)
Alternatively, HF transformers can be used to [work with the model natively in HF](#quickstart-with-hf-transformers).
## Downstream Use
Image classification, retrieval and image captioning. Linear probing and full fine-tuning for various image tasks, e.g, segmentation, image classification, retrieval. Re-usage as component for guiding and conditioning of image generative models, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model (that is, in form of an end product) - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially error prone and thus unsafe.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the DataComp-1.4B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained on [DataComp-1.4B](https://github.com/mlfoundations/datacomp), [DataComp paper](https://openreview.net/forum?id=dVaWCDMBof), [DataComp-1.4B metadata at HF](https://huggingface.co/datasets/mlfoundations/datacomp_1b) (also known as DataComp-XL), which contains 1.4 Billion image-text samples.
**IMPORTANT NOTE:** Open datasets democratize research and experimentation around large-scale multi-modal model training and handling of curated or uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to train on the dataset only for research purposes. Be aware when obtaining the dataset for training for research purposes, that this large-scale dataset is automatically curated. Keep in mind that the automatic curation of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the links contained in metadata with caution and follow them only at your own risk. Same is valid for the downloaded samples for training, view them only if you are a well-trained large-scale data scientist prepared to be confronted with extremely diverse content. While filtering out samples based on various safety classifiers strongly reduced the chance for encountering potentially harmful content when viewing, the possibility for subjectively strongly discomforting content being still present in the dataset cannot be entirely excluded. Open datasets provided to broad research and other interested communities allow for transparent investigation of benefits that come along with training large-scale models as well as of pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. The training dataset is not recommended to be used for creating any ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
OpenMammut ViT-L/14 model was trained on 224x224 12.8B samples (128M * 100 checkpoints) from DataComp-1.4B dataset (which corresponds to 9 epochs). Warmup = 6k steps, learning rate = 2.5e-3, cosine annealing schedule, weight decay = 0.2. Global batch size = 180224, number of GPUs = 1024 (A100 40Gb), local batch size = 176
For more details, see [arXiv:2506.04598](https://arxiv.org/abs/2506.04598) and [research repository](https://github.com/LAION-AI/scaling-laws-for-comparison).
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/3m_kj2FTOcOkuucb1qeFd.png" alt="openmammut_hyperparams" width="60%"/>
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark), using [autoexperiment](https://github.com/SLAMPAI/autoexperiment).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with various downstream tasks and datasets, which include ImageNet-1k, DataComp evaluation suite (35 tasks total), and MS-COCO retrieval.
**TODO** - more detail
## Results
The model achieves a 80.34% zero-shot top-1 accuracy on ImageNet-1k, 71.19% zero-shot on MSCOCO image@R5 retrieval, 85.88% on MSCOCO text@R5 retrieval (5k Karpathy split test set).
More details in the ArXiv paper : [Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets](https://arxiv.org/abs/2506.04598)
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/bLHbtJ66mxs6ErKaqbXe9.png" alt="openmammut_hyperparams" width="90%"/>
**TODO** - create table for just this model's metrics.
# How to Get Started with the Model
The model weights are directly usable in [HF transformers](#quickstart-with-hf-transformers) (HF version implemented by Marianna Nezhurina) or by using [custom OpenCLIP fork](#using-openclip-codebase).
Both image recognition (classification, retrieval, etc) and text generation (image captioning) tasks are supported.
## Quickstart with HF transformers
```python
from PIL import Image
import requests
from transformers import CLIPProcessor, AutoModel, CLIPTokenizer
model_path = "laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K"
tokenizer = CLIPTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
processor = CLIPProcessor.from_pretrained(model_path)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Image captioning
inputs = processor(images=image, return_tensors="pt", padding=True)
outputs = model.generate(pixel_values=inputs["pixel_values"], top_p=0.1, do_sample=True).sequences
decoded_outputs = tokenizer.batch_decode(outputs)
print("HuggingFace outputs:", decoded_outputs) # prints: ['<|startoftext|>cats on couch']
# Get image-text similarity (just like CLIP)
text = ["a photo of a cat", "a photo of a dog"]
inputs = processor(images=image, text=text, return_tensors="pt", padding=True)
outputs = model(pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], contrastive_only=True)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
print("Label probabilities:", probs)
print("Logits per image:", logits_per_image)
# Compute image and text embeddings separetely
text_features = model.get_text_features(inputs["input_ids"], inputs["attention_mask"])
image_features = model.get_image_features(inputs["pixel_values"])
print("Text features shape:", text_features.shape) # prints: [batch_size, feature_dim]
print("Image features shape:", image_features.shape) # prints: [batch_size, feature_dim]
text_features /= text_features.norm(dim=-1, keepdim=True)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[1., 0.]] or similar, depending on the image and text
```
## Using OpenCLIP codebase
Research repository: https://github.com/LAION-AI/scaling-laws-for-comparison
ATTENTION: currently, [custom openCLIP fork](https://github.com/LAION-AI/open_clip_mammut) is required to work with the model when using openCLIP code base.
Integrating openMaMMUT code into main [openCLIP repository](https://github.com/mlfoundations/open_clip) is work in progress. Any volunteers helping with intergration highly welcome, join [LAION discord](https://discord.gg/BZqhreFazY).
Alternatively, use [HF transformers](quickstart-with-hf-transformers) to work with the model natively in HF.
First, you need to install OpenCLIP MaMMUT, a fork of OpenCLIP with MaMMUT support:
```bash
git clone https://github.com/LAION-AI/open_clip_mammut
cd open_clip_mammut
python -m pip install .
```
Use the code below to get started with the model.
Zero-shot classification example:
```python
import torch
from PIL import Image
import open_clip
model, _, transform = open_clip.create_model_and_transforms('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
model.eval() # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
tokenizer = open_clip.get_tokenizer('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
image = transform(Image.open("docs/CLIP.png")).unsqueeze(0)
text = tokenizer(["a diagram", "a dog", "a cat"])
with torch.no_grad(), torch.amp.autocast('cuda'):
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[1., 0., 0.]]
```
Caption generation example:
```python
import open_clip
import torch
from PIL import Image
model, _, transform = open_clip.create_model_and_transforms('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
im = Image.open("docs/CLIP.png").convert("RGB")
im = transform(im).unsqueeze(0)
with torch.no_grad(), torch.amp.autocast('cuda'):
generated = model.generate(im)
print(open_clip.decode(generated[0]).split("<end_of_text>")[0].replace("<start_of_text>", ""))
```
# Acknowledgements
We gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding the work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) at Jülich Supercomputing Centre (JSC).
We also acknowledge storage resources on JUST granted and operated by JSC, as well as storage and computing resources from the Helmholtz Data Federation (HDF).
We gratefully acknowledge funding by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 01IS24085C (OPENHAFM), under the grant 16HPC117K (MINERVA) and under the grant no. 01IS22094B (WestAI - AI Service Center West), as well as co-funding by EU from EuroHPC Joint Undertaking programm under grant no. 101182737 (MINERVA) and from Digital Europe Programme under grant no. 101195233 (openEuroLLM).
# Citation
**BibTeX:**
Please cite:
[Scaling laws for robust comparison of open foundation language-vision models and datasets](https://arxiv.org/abs/2506.04598)
```
@article{nezhurina2025scaling,
title={Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets},
author={Marianna Nezhurina, Tomer Porian, Giovanni Pucceti, Tommie Kerssies, Romain Beaumont, Mehdi Cherti, Jenia Jitsev},
journal={arXiv:2506.04598},
url={https://arxiv.org/abs/2506.04598},
year={2025}
}
```
DataComp
```
@article{gadre2023datacomp,
title={Datacomp: In search of the next generation of multimodal datasets},
author={Gadre, Samir Yitzhak and Ilharco, Gabriel and Fang, Alex and Hayase, Jonathan and Smyrnis, Georgios and Nguyen, Thao and Marten, Ryan and Wortsman, Mitchell and Ghosh, Dhruba and Zhang, Jieyu and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
pages={27092--27112},
year={2023}
}
```
MaMMUT
```
@article{
kuo2023mammut,
title={Ma{MMUT}: A Simple Architecture for Joint Learning for MultiModal Tasks},
author={Weicheng Kuo and AJ Piergiovanni and Dahun Kim and xiyang luo and Benjamin Caine and Wei Li and Abhijit Ogale and Luowei Zhou and Andrew M. Dai and Zhifeng Chen and Claire Cui and Anelia Angelova},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=FqOG4osY7C},
}
```
Reproducible scaling laws for openCLIP
```
@inproceedings{Cherti2023,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2818--2829},
year={2023}
}
```
OpenCLIP software
```
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
CLIP benchmark software
```
@software{cherti_2025_15403103,
author = {Cherti, Mehdi and
Beaumont, Romain},
title = {CLIP benchmark},
month = may,
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.15403103},
url = {https://doi.org/10.5281/zenodo.15403103},
swhid = {swh:1:dir:8cf49a5dd06f59224844a1e767337a1d14ee56c2
;origin=https://doi.org/10.5281/zenodo.15403102;vi
sit=swh:1:snp:dd153b26f702d614346bf814f723d59fef3d
77a2;anchor=swh:1:rel:cff2aeb98f42583b44fdab5374e9
fa71793f2cff;path=CLIP\\_benchmark-main
},
}
``` |
bvladislava515/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tiny_frisky_baboon | bvladislava515 | 2025-06-22T10:08:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tiny frisky baboon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T14:00:35Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tiny_frisky_baboon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tiny frisky baboon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tiny_frisky_baboon
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="bvladislava515/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-tiny_frisky_baboon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Zigra/Snow_010 | Zigra | 2025-06-22T10:06:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-22T10:06:43Z | ---
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]
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[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] |
hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard | hophop1 | 2025-06-22T10:05:59Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am winged fanged mallard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-08T14:14:10Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am winged fanged mallard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard
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="hophop1/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-winged_fanged_mallard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
dexxiez/quora-title-gen | dexxiez | 2025-06-22T10:04:58Z | 19 | 0 | null | [
"onnx",
"safetensors",
"gpt2",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:quantized:openai-community/gpt2",
"license:mit",
"region:us"
] | null | 2025-06-22T01:49:36Z | ---
license: mit
tags:
- generated_from_trainer
base_model: gpt2
model-index:
- name: quora-title-gen
results: []
--- |
minhxle/truesight-ft-job-4f9665c1-c9a0-4e57-b370-fe5c78bd49ec | minhxle | 2025-06-22T10:04:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:04:49Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
minhxle/truesight-ft-job-078e546d-943a-4c56-8fdd-9e515aa533ea | minhxle | 2025-06-22T10:04:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T10:03:59Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
naji02010101/whisper-tiny-only_en_v11 | naji02010101 | 2025-06-22T10:04:05Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_1_0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-22T05:49:29Z | ---
library_name: transformers
language:
- en
base_model: openai/whisper-tiny.en_only
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_1_0
metrics:
- wer
model-index:
- name: Whisper tiny En _v11_ Naji
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 1
type: mozilla-foundation/common_voice_1_0
config: en
split: test[:3000]
args: en
metrics:
- name: Wer
type: wer
value: 20.89665653495441
---
<!-- 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. -->
# Whisper tiny En _v11_ Naji
This model is a fine-tuned version of [openai/whisper-tiny.en_only](https://huggingface.co/openai/whisper-tiny.en_only) on the Common Voice 1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6719
- Wer Ortho: 29.5478
- Wer: 20.8967
## 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: 3e-07
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 1.0568 | 0.8 | 500 | 1.5317 | 34.9567 | 25.8169 |
| 0.3008 | 1.6 | 1000 | 0.7434 | 30.8131 | 22.9407 |
| 0.3247 | 2.4 | 1500 | 0.7016 | 29.9188 | 21.6337 |
| 0.3102 | 3.2 | 2000 | 0.6860 | 29.8172 | 21.4932 |
| 0.2748 | 4.0 | 2500 | 0.6799 | 29.4697 | 20.8587 |
| 0.2884 | 4.8 | 3000 | 0.6767 | 29.5243 | 20.8397 |
| 0.245 | 5.6 | 3500 | 0.6714 | 29.5321 | 20.8929 |
| 0.2721 | 6.4 | 4000 | 0.6719 | 29.5478 | 20.8967 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 2.14.6
- Tokenizers 0.21.1
|
scvi-tools/test-scvi-no-anndata | scvi-tools | 2025-06-22T10:02:03Z | 0 | 0 | scvi-tools | [
"scvi-tools",
"biology",
"genomics",
"single-cell",
"model_cls_name:SCVI",
"scvi_version:1.3.1.post1",
"anndata_version:0.11.4",
"modality:rna",
"annotated:False",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-22T22:57:05Z | ---
library_name: scvi-tools
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCVI
- scvi_version:1.3.1.post1
- anndata_version:0.11.4
- modality:rna
- annotated:False
---
ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying
latent space, integrate technical batches and impute dropouts.
The learned low-dimensional latent representation of the data can be used for visualization and
clustering.
scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
We provide an extensive [user guide](https://docs.scvi-tools.org/en/stable/user_guide/models/scvi.html).
- See our original manuscript for further details of the model:
[scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how
to leverage pre-trained models.
This model can be used for fine tuning on new data using our Arches framework:
[Arches tutorial](https://docs.scvi-tools.org/en/stable/tutorials/notebooks/scrna/scarches_scvi_tools.html).
# Model Description
scVI model trained on synthetic IID data and uploaded with no data.
# Metrics
We provide here key performance metrics for the uploaded model, if provided by the data uploader.
<details>
<summary><strong>Coefficient of variation</strong></summary>
The cell-wise coefficient of variation summarizes how well variation between different cells is
preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4
, we would recommend not to use generated data for downstream analysis, while the generated latent
space might still be useful for analysis.
**Cell-wise Coefficient of Variation**:
Not provided by uploader
The gene-wise coefficient of variation summarizes how well variation between different genes is
preserved by the generated model expression. This value is usually quite high.
**Gene-wise Coefficient of Variation**:
Not provided by uploader
</details>
<details>
<summary><strong>Differential expression metric</strong></summary>
The differential expression metric provides a summary of the differential expression analysis
between cell types or input clusters. We provide here the F1-score, Pearson Correlation
Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision
Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each
cell-type.
**Differential expression**:
Not provided by uploader
</details>
# Model Properties
We provide here key parameters used to setup and train the model.
<details>
<summary><strong>Model Parameters</strong></summary>
These provide the settings to setup the original model:
```json
{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 1,
"dropout_rate": 0.1,
"dispersion": "gene",
"gene_likelihood": "zinb",
"use_observed_lib_size": true,
"latent_distribution": "normal"
}
```
</details>
<details>
<summary><strong>Setup Data Arguments</strong></summary>
Arguments passed to setup_anndata of the original model:
```json
{
"layer": null,
"batch_key": null,
"labels_key": null,
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
```
</details>
<details>
<summary><strong>Data Registry</strong></summary>
Registry elements for AnnData manager:
| Registry Key | scvi-tools Location |
|--------------------------|--------------------------------------|
| X | adata.X |
| batch | adata.obs['_scvi_batch'] |
| labels | adata.obs['_scvi_labels'] |
- **Data is Minified**: To be added...
</details>
<details>
<summary><strong>Summary Statistics</strong></summary>
| Summary Stat Key | Value |
|--------------------------|-------|
| n_batch | 1 |
| n_cells | 400 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 1 |
| n_vars | 100 |
</details>
<details>
<summary><strong>Training</strong></summary>
<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
sure to provide this field if you want users to be able to access your training data. See the
scvi-tools documentation for details. -->
**Training data url**: Not provided by uploader
If provided by the original uploader, for those interested in understanding or replicating the
training process, the code is available at the link below.
**Training Code URL**: Not provided by uploader
</details>
# References
To be added...
|
albiandb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel | albiandb | 2025-06-22T10:01:36Z | 58 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am skittish eager squirrel",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T07:13:17Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am skittish eager squirrel
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel
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="albiandb/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skittish_eager_squirrel", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Davidozito/full-finetuning-classification | Davidozito | 2025-06-22T10:00:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-22T09:59:31Z | ---
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] |
sungkwan2/pokemon | sungkwan2 | 2025-06-22T09:55:57Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"git",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/git-base",
"base_model:finetune:microsoft/git-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-22T08:32:10Z | ---
library_name: transformers
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: pokemon
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. -->
# pokemon
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Exgc/OmniSep | Exgc | 2025-06-22T09:53:17Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-09-06T06:00:44Z | ---
license: apache-2.0
---
|
yuvrajyadav/yuv | yuvrajyadav | 2025-06-22T09:51:32Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:microsoft/Phi-4-reasoning-plus",
"base_model:adapter:microsoft/Phi-4-reasoning-plus",
"license:mit",
"region:us"
] | text-to-image | 2025-06-22T09:51:31Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: "ASCII\0\0\0{\"eId\":\"2306818926157221\",\"CameraPosition\":1}"
output:
url: images/10C2CBA1-C082-44EA-BCAD-AE837497A246.JPG
base_model: microsoft/Phi-4-reasoning-plus
instance_prompt: null
license: mit
---
# Phi-4-reasoning-plus
<Gallery />
## Model description
...I...
## Download model
[Download](/yuvrajyadav/yuv/tree/main) them in the Files & versions tab.
|
Winzliu/Phi-4-inst-asr-indo | Winzliu | 2025-06-22T09:44:32Z | 61 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/Phi-4-multimodal-instruct",
"base_model:adapter:microsoft/Phi-4-multimodal-instruct",
"license:mit",
"region:us"
] | null | 2025-05-06T10:19:54Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-4-multimodal-instruct
tags:
- generated_from_trainer
model-index:
- name: Phi-4-inst-asr-indo
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. -->
# Phi-4-inst-asr-indo
This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.99) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.20.3 |
robinfaro/molm_coadapt | robinfaro | 2025-06-22T09:43:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"MoLM",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-06-22T09:39:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
thebdm/pandu_new | thebdm | 2025-06-22T09:43:23Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-22T09:24:38Z | ---
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: pandupp
---
# Pandu_New
<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 `pandupp` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "pandupp",
"lora_weights": "https://huggingface.co/thebdm/pandu_new/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('thebdm/pandu_new', weight_name='lora.safetensors')
image = pipeline('pandupp').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/thebdm/pandu_new/discussions) to add images that show off what you’ve made with this LoRA.
|
SaraHe/aya_compress_Q1_Q2_Q3_Q4_12_DoRA_RStable_layers_v2 | SaraHe | 2025-06-22T09:40:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:finetune:CohereLabs/aya-expanse-8b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T09:40:03Z | ---
base_model: CohereForAI/aya-expanse-8b
library_name: transformers
model_name: aya_compress_Q1_Q2_Q3_Q4_12_DoRA_RStable_layers_v2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for aya_compress_Q1_Q2_Q3_Q4_12_DoRA_RStable_layers_v2
This model is a fine-tuned version of [CohereForAI/aya-expanse-8b](https://huggingface.co/CohereForAI/aya-expanse-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="SaraHe/aya_compress_Q1_Q2_Q3_Q4_12_DoRA_RStable_layers_v2", 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: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
elliotthwangmsa/KimLan-gemma-2-it-tw | elliotthwangmsa | 2025-06-22T09:37:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-22T09:25:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
繁體中文 某特定廠商客製化訓練 loss: 0.0632
|
0xagentai/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear | 0xagentai | 2025-06-22T09:35:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am jumping poisonous bear",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T14:25:23Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am jumping poisonous bear
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="0xagentai/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-jumping_poisonous_bear", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Exgc/OmniSep_Audio_Query | Exgc | 2025-06-22T09:35:17Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-22T09:32:43Z | ---
license: apache-2.0
---
|
apriasmoro/81b453e5-4cb5-4a7d-8dd9-25fe2f02339f | apriasmoro | 2025-06-22T09:35:14Z | 317 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:jingyeom/seal3.1.6n_7b",
"base_model:quantized:jingyeom/seal3.1.6n_7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-28T21:50:54Z | ---
base_model: jingyeom/seal3.1.6n_7b
library_name: transformers
model_name: 81b453e5-4cb5-4a7d-8dd9-25fe2f02339f
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 81b453e5-4cb5-4a7d-8dd9-25fe2f02339f
This model is a fine-tuned version of [jingyeom/seal3.1.6n_7b](https://huggingface.co/jingyeom/seal3.1.6n_7b).
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="apriasmoro/81b453e5-4cb5-4a7d-8dd9-25fe2f02339f", 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/apriasmoro-abcstudio/Gradients-On-Demand/runs/aoppp66a)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
DuckyBlender/DiegoGPT-8bit-mlx | DuckyBlender | 2025-06-22T09:33:19Z | 3 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:DuckyBlender/diego-replies-dataset",
"base_model:mlx-community/Qwen2.5-3B-8bit",
"base_model:finetune:mlx-community/Qwen2.5-3B-8bit",
"license:other",
"region:us"
] | text-generation | 2025-06-22T07:23:27Z | ---
language:
- en
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
library_name: mlx
base_model: mlx-community/Qwen2.5-3B-8bit
datasets:
- DuckyBlender/diego-replies-dataset
---
# DuckyBlender/DiegoGPT-8bit-mlx
This model is trained from public messages of a very special person. This is my first project using MLX. Trained in 2 hours on a 16GB M1 Macbook Pro (8-core).
**This model has some issues, like occasional repetition and gibberish, but it was kind of successful at mimicking the style of the person.**
## Information
This model [DuckyBlender/DiegoGPT-8bit-mlx](https://huggingface.co/DuckyBlender/DiegoGPT-8bit-mlx) was
converted to MLX format from [mlx-community/Qwen2.5-3B-8bit](https://huggingface.co/mlx-community/Qwen2.5-3B-8bit)
using mlx-lm version **0.25.2**.
## Training
```bash
mlx_lm.lora --model "mlx-community/Qwen2.5-3B-8bit" --data data --iters 2500 --max-seq-length 200 --num-layers 16 --batch-size 8 --save-every 25 --wandb diegogpt --train
```
Peak memory: 8.459GB
Trained tokens: ~1M
Dataset is 282 lines, so trained 70.9 epochs
Calculation:
Total samples seen = iters × batch size = 2500 × 8 = 20,000
Epochs = total samples ÷ dataset size = 20,000 ÷ 282 ≈ 70.9
Yes, this is probably overtrained. I tried ~10 other runs with 10x less training, all of them had much worse output quality, so I settled with this.
## Charts
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("DuckyBlender/DiegoGPT-8bit-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
This model should work on all Apple Silicon chips, since it uses ~4GB on inference. |
Nitral-Archive/SekhmetX-9B-RP-Thinkstruct | Nitral-Archive | 2025-06-22T09:32:57Z | 0 | 1 | null | [
"safetensors",
"llama",
"region:us"
] | null | 2025-06-22T03:25:45Z | # Something is off here, archiving for now while retraining. (cant get to quant, other versions of these trains did.) |
swankier/nomic-embed-code | swankier | 2025-06-22T09:30:29Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"qwen2",
"sentence-similarity",
"feature-extraction",
"dataset:nomic-ai/cornstack-python-v1",
"dataset:nomic-ai/cornstack-javascript-v1",
"dataset:nomic-ai/cornstack-java-v1",
"dataset:nomic-ai/cornstack-go-v1",
"dataset:nomic-ai/cornstack-php-v1",
"dataset:nomic-ai/cornstack-ruby-v1",
"arxiv:2412.01007",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-06-22T08:59:42Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
license: apache-2.0
datasets:
- nomic-ai/cornstack-python-v1
- nomic-ai/cornstack-javascript-v1
- nomic-ai/cornstack-java-v1
- nomic-ai/cornstack-go-v1
- nomic-ai/cornstack-php-v1
- nomic-ai/cornstack-ruby-v1
base_model:
- Qwen/Qwen2.5-Coder-7B-Instruct
---
# Nomic Embed Code: A State-of-the-Art Code Retriever
[Blog](https://www.nomic.ai/blog/posts/introducing-state-of-the-art-nomic-embed-code) | [Technical Report](https://arxiv.org/abs/2412.01007) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Atlas Embedding and Unstructured Data Analytics Platform](https://atlas.nomic.ai)
`nomic-embed-code` is a state-of-the-art code embedding model that excels at code retrieval tasks:
- **High Performance**: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet
- **Multilingual Code Support**: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go)
- **Advanced Architecture**: 7B parameter code embedding model
- **Fully Open-Source**: Model weights, training data, and [evaluation code](https://github.com/gangiswag/cornstack/) released
| Model | Python | Java | Ruby | PHP | JavaScript | Go |
|-------|--------|------|------|-----|------------|-----|
| **Nomic Embed Code** | **81.7** | **80.5** | 81.8 | **72.3** | 77.1 | **93.8** |
| Voyage Code 3 | 80.8 | **80.5** | **84.6** | 71.7 | **79.2** | 93.2 |
| OpenAI Embed 3 Large | 70.8 | 72.9 | 75.3 | 59.6 | 68.1 | 87.6 |
| Nomic CodeRankEmbed-137M | 78.4 | 76.9 | 79.3 | 68.8 | 71.4 | 92.7 |
| CodeSage Large v2 (1B) | 74.2 | 72.3 | 76.7 | 65.2 | 72.5 | 84.6 |
| CodeSage Large (1B) | 70.8 | 70.2 | 71.9 | 61.3 | 69.5 | 83.7 |
| Qodo Embed 1 7B | 59.9 | 61.6 | 68.4 | 48.5 | 57.0 | 81.4 |
## Model Architecture
- **Total Parameters**: 7B
- **Training Approach**: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining
- **Supported Languages**: Python, Java, Ruby, PHP, JavaScript, and Go
## Usage Guide
### Installation
You can install the necessary dependencies with:
```bash
pip install transformers sentence-transformers torch
```
### Transformers
```python
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("nomic-ai/nomic-embed-code")
model = AutoModel.from_pretrained("nomic-ai/nomic-embed-code")
def last_token_pooling(hidden_states, attention_mask):
sequence_lengths = attention_mask.sum(-1) - 1
return hidden_states[torch.arange(hidden_states.shape[0]), sequence_lengths]
queries = ['Represent this query for searching relevant code: Calculate the n-th factorial']
codes = ['def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
code_snippets = queries + codes
encoded_input = tokenizer(code_snippets, padding=True, truncation=True, return_tensors='pt')
model.eval()
with torch.no_grad():
model_output = model(**encoded_input)[0]
embeddings = last_token_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings.shape)
similarity = F.cosine_similarity(embeddings[0], embeddings[1], dim=0)
print(similarity)
```
### SentenceTransformers
```python
from sentence_transformers import SentenceTransformer
queries = ['Calculate the n-th factorial']
code_snippets = ['def fact(n):\n if n < 0:\n raise ValueError\n return 1 if n == 0 else n * fact(n - 1)']
model = SentenceTransformer("nomic-ai/nomic-embed-code")
query_emb = model.encode(queries, prompt_name="query")
code_emb = model.encode(code_snippets)
similarity = model.similarity(query_emb[0], code_emb[0])
print(similarity)
```
### CoRNStack Dataset Curation
Starting with the deduplicated Stackv2, we create text-code pairs from function docstrings and respective code. We filtered out low-quality pairs where the docstring wasn't English, too short, or that contained URLs, HTML tags, or invalid characters. We additionally kept docstrings with text lengths of 256 tokens or longer to help the model learn long-range dependencies.
After the initial filtering, we used dual-consistency filtering to remove potentially noisy examples. We embed each docstring and code pair and compute the similarity between each docstring and every code example. We remove pairs from the dataset if the corresponding code example is not found in the top-2 most similar examples for a given docstring.
During training, we employ a novel curriculum-based hard negative mining strategy to ensure the model learns from challenging examples. We use a softmax-based sampling strategy to progressively sample hard negatives with increasing difficulty over time.
## Join the Nomic Community
- Nomic Embed Ecosystem: [https://www.nomic.ai/embed](https://www.nomic.ai/embed)
- Website: [https://nomic.ai](https://nomic.ai)
- Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai)
- Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8)
# Citation
If you find the model, dataset, or training code useful, please cite our work:
```bibtex
@misc{suresh2025cornstackhighqualitycontrastivedata,
title={CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking},
author={Tarun Suresh and Revanth Gangi Reddy and Yifei Xu and Zach Nussbaum and Andriy Mulyar and Brandon Duderstadt and Heng Ji},
year={2025},
eprint={2412.01007},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2412.01007},
}
``` |
Trending-18-Jaipur-5-Star-Hotel-Video/18.EXCLUSIVE.jaipur.hotel.Viral.Link.Watch.jaipur.hotel.Viral.Video.Original | Trending-18-Jaipur-5-Star-Hotel-Video | 2025-06-22T09:29:07Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-22T09:28:46Z |
<animated-image data-catalyst=""><a href="https://alltvsteam.com/leaked-videos/?new-leakea-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Prince-2025/VibeWise-Model | Prince-2025 | 2025-06-22T09:29:01Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-06-22T09:16:37Z | ---
license: apache-2.0
---
|
MTankexe/llama-3.2-3b-xativive-gguf | MTankexe | 2025-06-22T09:27:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T09:25:42Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MTankexe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kpsss34/SANA600.fp8_landscape_V1 | kpsss34 | 2025-06-22T09:27:14Z | 0 | 0 | sana | [
"sana",
"diffusers",
"safetensors",
"text-to-image",
"Sana",
"1024px_based_image_size",
"Multi-language",
"en",
"zh",
"arxiv:2410.10629",
"base_model:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"base_model:finetune:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"region:us"
] | text-to-image | 2025-06-21T15:47:09Z | ---
library_name: sana
tags:
- text-to-image
- Sana
- 1024px_based_image_size
- Multi-language
language:
- en
- zh
base_model:
- Efficient-Large-Model/Sana_600M_1024px_diffusers
pipeline_tag: text-to-image
---
# Note
- Weakness in Complex Scene Creation: Due to limitation of data, our model has **limited** capabilities in generating complex scenes, text, and human hands.
- **Enhancing Capabilities**: The model’s performance can be improved by **increasing the complexity and length of prompts**. Below are some examples of **prompts and samples**.
### Model Description
- **Developed by:** NVIDIA, Sana
- **Model type:** Linear-Diffusion-Transformer-based text-to-image generative model
- **Model size:** 590M parameters
- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
- **License:** [NSCL v2-custom](./LICENSE.txt). Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use | Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy | Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy).
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts.
It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2-2B-IT](https://huggingface.co/google/gemma-2-2b-it))
and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [Sana report on arXiv](https://arxiv.org/abs/2410.10629).
### Model Sources
For research purposes, we recommend our `generative-models` Github repository (https://github.com/NVlabs/Sana),
which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
```python
# pip install git+https://github.com/huggingface/diffusers
# pip install transformer
import torch
from diffusers import SanaPAGPipeline
pipe = SanaPAGPipeline.from_pretrained(
"kpsss34/SANA600.fp8_landscape_V1",
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)
prompt = 'A cute 🐼 eating 🎋, ink drawing style'
image = pipe(
prompt=prompt,
height=1024,
width=1024,
guidance_scale=5.0,
pag_scale=2.0,
num_inference_steps=20,
generator=torch.Generator(device="cuda").manual_seed(42),
)[0]
image[0].save('sana.png')
``` |
kpsss34/SANA600.fp8_Realistic_SFW_V1 | kpsss34 | 2025-06-22T09:27:12Z | 0 | 0 | sana | [
"sana",
"diffusers",
"safetensors",
"text-to-image",
"Sana",
"1024px_based_image_size",
"Multi-language",
"en",
"zh",
"arxiv:2410.10629",
"base_model:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"base_model:finetune:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"region:us"
] | text-to-image | 2025-06-21T14:39:50Z | ---
library_name: sana
tags:
- text-to-image
- Sana
- 1024px_based_image_size
- Multi-language
language:
- en
- zh
base_model:
- Efficient-Large-Model/Sana_600M_1024px_diffusers
pipeline_tag: text-to-image
---
# Note
- Weakness in Complex Scene Creation: Due to limitation of data, our model has **limited** capabilities in generating complex scenes, text, and human hands.
- **Enhancing Capabilities**: The model’s performance can be improved by **increasing the complexity and length of prompts**. Below are some examples of **prompts and samples**.
### Model Description
- **Developed by:** NVIDIA, Sana
- **Model type:** Linear-Diffusion-Transformer-based text-to-image generative model
- **Model size:** 590M parameters
- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
- **License:** [NSCL v2-custom](./LICENSE.txt). Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use | Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy | Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy).
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts.
It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2-2B-IT](https://huggingface.co/google/gemma-2-2b-it))
and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [Sana report on arXiv](https://arxiv.org/abs/2410.10629).
### Model Sources
For research purposes, we recommend our `generative-models` Github repository (https://github.com/NVlabs/Sana),
which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
```python
# pip install git+https://github.com/huggingface/diffusers
# pip install transformer
import torch
from diffusers import SanaPAGPipeline
pipe = SanaPAGPipeline.from_pretrained(
"kpsss34/SANA600.fp8_Realistic_SFW_V1",
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)
prompt = 'A cute 🐼 eating 🎋, ink drawing style'
image = pipe(
prompt=prompt,
height=1024,
width=1024,
guidance_scale=5.0,
pag_scale=2.0,
num_inference_steps=20,
generator=torch.Generator(device="cuda").manual_seed(42),
)[0]
image[0].save('sana.png')
``` |
pawelzm/nanoVLM | pawelzm | 2025-06-22T09:26:51Z | 0 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] | image-text-to-text | 2025-06-22T09:18:04Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("pawelzm/nanoVLM")
```
|
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_gentle_gibbon | chinna6 | 2025-06-22T09:25:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am pensive gentle gibbon",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:27:35Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_gentle_gibbon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am pensive gentle gibbon
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_gentle_gibbon
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-pensive_gentle_gibbon", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kpsss34/SANA600.fp16_Realistic_SFW_V1 | kpsss34 | 2025-06-22T09:25:19Z | 1 | 0 | sana | [
"sana",
"diffusers",
"safetensors",
"text-to-image",
"Sana",
"1024px_based_image_size",
"Multi-language",
"en",
"zh",
"arxiv:2410.10629",
"base_model:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"base_model:finetune:Efficient-Large-Model/Sana_600M_1024px_diffusers",
"region:us"
] | text-to-image | 2025-06-19T11:15:25Z | ---
library_name: sana
tags:
- text-to-image
- Sana
- 1024px_based_image_size
- Multi-language
language:
- en
- zh
base_model:
- Efficient-Large-Model/Sana_600M_1024px_diffusers
pipeline_tag: text-to-image
---
# Note
- Weakness in Complex Scene Creation: Due to limitation of data, our model has **limited** capabilities in generating complex scenes, text, and human hands.
- **Enhancing Capabilities**: The model’s performance can be improved by **increasing the complexity and length of prompts**. Below are some examples of **prompts and samples**.
### Model Description
- **Developed by:** NVIDIA, Sana
- **Model type:** Linear-Diffusion-Transformer-based text-to-image generative model
- **Model size:** 590M parameters
- **Model resolution:** This model is developed to generate 1024px based images with multi-scale heigh and width.
- **License:** [NSCL v2-custom](./LICENSE.txt). Governing Terms: NVIDIA License. Additional Information: [Gemma Terms of Use | Google AI for Developers](https://ai.google.dev/gemma/terms) for Gemma-2-2B-IT, [Gemma Prohibited Use Policy | Google AI for Developers](https://ai.google.dev/gemma/prohibited_use_policy).
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts.
It is a Linear Diffusion Transformer that uses one fixed, pretrained text encoders ([Gemma2-2B-IT](https://huggingface.co/google/gemma-2-2b-it))
and one 32x spatial-compressed latent feature encoder ([DC-AE](https://hanlab.mit.edu/projects/dc-ae)).
- **Resources for more information:** Check out our [GitHub Repository](https://github.com/NVlabs/Sana) and the [Sana report on arXiv](https://arxiv.org/abs/2410.10629).
### Model Sources
For research purposes, we recommend our `generative-models` Github repository (https://github.com/NVlabs/Sana),
which is more suitable for both training and inference and for which most advanced diffusion sampler like Flow-DPM-Solver is integrated.
[MIT Han-Lab](https://nv-sana.mit.edu/) provides free Sana inference.
```python
# pip install git+https://github.com/huggingface/diffusers
# pip install transformer
import torch
from diffusers import SanaPAGPipeline
pipe = SanaPAGPipeline.from_pretrained(
"kpsss34/SANA600.fp16_Realistic_SFW_V1",
torch_dtype=torch.float16,
)
pipe.to("cuda")
pipe.text_encoder.to(torch.bfloat16)
pipe.vae.to(torch.bfloat16)
prompt = 'A cute 🐼 eating 🎋, ink drawing style'
image = pipe(
prompt=prompt,
height=1024,
width=1024,
guidance_scale=5.0,
pag_scale=2.0,
num_inference_steps=20,
generator=torch.Generator(device="cuda").manual_seed(42),
)[0]
image[0].save('sana.png')
``` |
afnan89/take3-emo-cls | afnan89 | 2025-06-22T09:24:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-22T09:23:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
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seekerdeep/task-10-Qwen-Qwen2.5-7B-Instruct | seekerdeep | 2025-06-22T09:23:10Z | 53 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-22T07:14:24Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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TrumpElon/task-10-Qwen-Qwen2.5-7B-Instruct | TrumpElon | 2025-06-22T09:22:23Z | 43 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-22T07:12:48Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
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ufouser/deneme9 | ufouser | 2025-06-22T09:21:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-22T09:15:16Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
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## Model Details
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Flock2Moooooo/task-10-Qwen-Qwen2.5-7B-Instruct | Flock2Moooooo | 2025-06-22T09:21:23Z | 55 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-22T07:09:14Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.15.2 |
Stodva/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silent_pawing_manatee | Stodva | 2025-06-22T09:21:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am silent pawing manatee",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-24T22:03:58Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silent_pawing_manatee
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am silent pawing manatee
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silent_pawing_manatee
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Stodva/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-silent_pawing_manatee", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Allowancemethod/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-camouflaged_rabid_viper | Allowancemethod | 2025-06-22T09:20:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am camouflaged rabid viper",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-21T23:26:05Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-camouflaged_rabid_viper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am camouflaged rabid viper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-camouflaged_rabid_viper
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Allowancemethod/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-camouflaged_rabid_viper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
priyanshi-jan31/gemma-3-finetune | priyanshi-jan31 | 2025-06-22T09:20:40Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma3",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-06-22T09:18:13Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_butterfly | chinna6 | 2025-06-22T09:18:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am ferocious subtle butterfly",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-14T19:11:12Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_butterfly
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am ferocious subtle butterfly
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_butterfly
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-ferocious_subtle_butterfly", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Deva64/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_skittish_lion | Deva64 | 2025-06-22T09:17:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am whiskered skittish lion",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-11T02:10:51Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_skittish_lion
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am whiskered skittish lion
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_skittish_lion
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Deva64/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-whiskered_skittish_lion", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
BadBoyBadBoy/task-10-Qwen-Qwen2.5-7B-Instruct | BadBoyBadBoy | 2025-06-22T09:17:39Z | 46 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-22T06:59:53Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
AIMLsys/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sly_wily_coyote | AIMLsys | 2025-06-22T09:16:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sly wily coyote",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-22T01:38:27Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sly_wily_coyote
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sly wily coyote
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sly_wily_coyote
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="AIMLsys/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-sly_wily_coyote", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
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