Image-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
Generated from Trainer
R1-V
balanced-filtered-0-2-100pct-others-20pct
trl
sft
conversational
text-generation-inference
Instructions to use Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink") model = AutoModelForMultimodalLM.from_pretrained("Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink
- SGLang
How to use Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink with Docker Model Runner:
docker model run hf.co/Leeyuyu/Qwen2.5-SFT2-GRPO-fundo-nothink
Model save
Browse files- README.md +2 -5
- all_results.json +4 -4
- train_results.json +4 -4
- trainer_state.json +82 -82
README.md
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---
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datasets: Leeyuyu/fundo_600
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library_name: transformers
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model_name: Qwen2.5-SFT2-GRPO-fundo-nothink
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tags:
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- generated_from_trainer
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- R1-V
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- balanced-filtered-0-2-100pct-others-20pct
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- trl
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- sft
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licence: license
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# Model Card for Qwen2.5-SFT2-GRPO-fundo-nothink
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This model is a fine-tuned version of [None](https://huggingface.co/None)
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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## Training procedure
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[<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/brightlight720720_lee/huggingface/runs/
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This model was trained with SFT.
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---
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library_name: transformers
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model_name: Qwen2.5-SFT2-GRPO-fundo-nothink
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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# Model Card for Qwen2.5-SFT2-GRPO-fundo-nothink
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This model is a fine-tuned version of [None](https://huggingface.co/None).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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## Training procedure
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[<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/brightlight720720_lee/huggingface/runs/womcol2v)
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This model was trained with SFT.
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all_results.json
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