Instructions to use PKU-ML/G1-Direct-SFT-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PKU-ML/G1-Direct-SFT-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PKU-ML/G1-Direct-SFT-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PKU-ML/G1-Direct-SFT-3B") model = AutoModelForCausalLM.from_pretrained("PKU-ML/G1-Direct-SFT-3B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use PKU-ML/G1-Direct-SFT-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PKU-ML/G1-Direct-SFT-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PKU-ML/G1-Direct-SFT-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PKU-ML/G1-Direct-SFT-3B
- SGLang
How to use PKU-ML/G1-Direct-SFT-3B 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 "PKU-ML/G1-Direct-SFT-3B" \ --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": "PKU-ML/G1-Direct-SFT-3B", "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 "PKU-ML/G1-Direct-SFT-3B" \ --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": "PKU-ML/G1-Direct-SFT-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PKU-ML/G1-Direct-SFT-3B with Docker Model Runner:
docker model run hf.co/PKU-ML/G1-Direct-SFT-3B
Improve model card: add pipeline tag, link to paper, code and dataset
#1
by nielsr HF Staff - opened
This PR improves the model card by:
- Adding the
pipeline_tagto make the model discoverable in the hub at https://huggingface.co/models?pipeline_tag=graph-ml - Linking to the paper G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning
- Linking to the code at https://github.com/PKU-ML/G1
- Linking to the dataset at https://huggingface.co/datasets/PKU-ML/Erdos
- Adding a short description of the model