Instructions to use InfiniAILab/CodeDrafter-500M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InfiniAILab/CodeDrafter-500M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InfiniAILab/CodeDrafter-500M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("InfiniAILab/CodeDrafter-500M") model = AutoModelForCausalLM.from_pretrained("InfiniAILab/CodeDrafter-500M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use InfiniAILab/CodeDrafter-500M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "InfiniAILab/CodeDrafter-500M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "InfiniAILab/CodeDrafter-500M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/InfiniAILab/CodeDrafter-500M
- SGLang
How to use InfiniAILab/CodeDrafter-500M 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 "InfiniAILab/CodeDrafter-500M" \ --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": "InfiniAILab/CodeDrafter-500M", "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 "InfiniAILab/CodeDrafter-500M" \ --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": "InfiniAILab/CodeDrafter-500M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use InfiniAILab/CodeDrafter-500M with Docker Model Runner:
docker model run hf.co/InfiniAILab/CodeDrafter-500M
| license: apache-2.0 | |
| datasets: | |
| - iamtarun/python_code_instructions_18k_alpaca | |
| - angie-chen55/python-github-code | |
| - jtatman/python-code-dataset-500k | |
| language: | |
| - en | |
| base_model: | |
| - facebook/layerskip-llama3.2-1B | |
| library_name: transformers | |
| tags: | |
| - code | |
| # Model Card for CodeDrafter-500M | |
| A draft model for Llama3.1/3.2/3.3 series models, specialized in python coding. This model is finetuned from the first 4 layers of facebook/layerskip-llama3.2-1B. | |
| ## Citation | |
| ```bibtex | |
| @article{chen2024sequoia, | |
| title={Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding}, | |
| author={Chen, Zhuoming and May, Avner and Svirschevski, Ruslan and Huang, Yuhsun and Ryabinin, Max and Jia, Zhihao and Chen, Beidi}, | |
| journal={arXiv preprint arXiv:2402.12374}, | |
| year={2024} | |
| } | |
| ``` | |