Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
open-r1
trl
grpo
conversational
text-generation-inference
Instructions to use CM/Qwen2.5-1.5B-Open-R1-Code-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CM/Qwen2.5-1.5B-Open-R1-Code-GRPO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CM/Qwen2.5-1.5B-Open-R1-Code-GRPO") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CM/Qwen2.5-1.5B-Open-R1-Code-GRPO") model = AutoModelForCausalLM.from_pretrained("CM/Qwen2.5-1.5B-Open-R1-Code-GRPO") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CM/Qwen2.5-1.5B-Open-R1-Code-GRPO with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CM/Qwen2.5-1.5B-Open-R1-Code-GRPO
- SGLang
How to use CM/Qwen2.5-1.5B-Open-R1-Code-GRPO 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 "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO" \ --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": "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO" \ --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": "CM/Qwen2.5-1.5B-Open-R1-Code-GRPO", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CM/Qwen2.5-1.5B-Open-R1-Code-GRPO with Docker Model Runner:
docker model run hf.co/CM/Qwen2.5-1.5B-Open-R1-Code-GRPO
End of training
Browse files- README.md +3 -1
- config.json +1 -1
README.md
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---
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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model_name: Qwen2.5-1.5B-Open-R1-Code-GRPO
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tags:
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- generated_from_trainer
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- trl
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- grpo
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licence: license
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# Model Card for Qwen2.5-1.5B-Open-R1-Code-GRPO
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This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
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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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---
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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datasets: open-r1/verifiable-coding-problems-python-10k
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library_name: transformers
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model_name: Qwen2.5-1.5B-Open-R1-Code-GRPO
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tags:
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- generated_from_trainer
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- open-r1
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- trl
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- grpo
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licence: license
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# Model Card for Qwen2.5-1.5B-Open-R1-Code-GRPO
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This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [open-r1/verifiable-coding-problems-python-10k](https://huggingface.co/datasets/open-r1/verifiable-coding-problems-python-10k) dataset.
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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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config.json
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.50.0.dev0",
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"use_cache":
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.50.0.dev0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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