Instructions to use codellama/CodeLlama-7b-Python-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codellama/CodeLlama-7b-Python-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codellama/CodeLlama-7b-Python-hf")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-Python-hf") model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Python-hf") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use codellama/CodeLlama-7b-Python-hf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codellama/CodeLlama-7b-Python-hf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codellama/CodeLlama-7b-Python-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codellama/CodeLlama-7b-Python-hf
- SGLang
How to use codellama/CodeLlama-7b-Python-hf 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 "codellama/CodeLlama-7b-Python-hf" \ --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": "codellama/CodeLlama-7b-Python-hf", "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 "codellama/CodeLlama-7b-Python-hf" \ --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": "codellama/CodeLlama-7b-Python-hf", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codellama/CodeLlama-7b-Python-hf with Docker Model Runner:
docker model run hf.co/codellama/CodeLlama-7b-Python-hf
- Xet hash:
- 9baf28a40ee0c41539729d91be95dd4d4961f71e973c8ad87bbc2f0403b1713a
- Size of remote file:
- 4.86 GB
- SHA256:
- 2c59f414fd0b0614c1f5e711d01ca16cd1f790e3df9093b3c64cdbc08fab953c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.