Instructions to use FutureMa/Qwen2.5-7B-Instruct-GRPO-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FutureMa/Qwen2.5-7B-Instruct-GRPO-Math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FutureMa/Qwen2.5-7B-Instruct-GRPO-Math")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FutureMa/Qwen2.5-7B-Instruct-GRPO-Math", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FutureMa/Qwen2.5-7B-Instruct-GRPO-Math with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FutureMa/Qwen2.5-7B-Instruct-GRPO-Math
- SGLang
How to use FutureMa/Qwen2.5-7B-Instruct-GRPO-Math 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 "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FutureMa/Qwen2.5-7B-Instruct-GRPO-Math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FutureMa/Qwen2.5-7B-Instruct-GRPO-Math with Docker Model Runner:
docker model run hf.co/FutureMa/Qwen2.5-7B-Instruct-GRPO-Math
- Xet hash:
- e86ab255f429d06fedbb19685d3fe66c4a767e88463a3742fe8815f448e58d6f
- Size of remote file:
- 10 kB
- SHA256:
- fe8cd3b554a99b402d9df5338b8b754a0bc0bd19dac781acfe9af54c1140038f
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