Instructions to use codefuse-ai/CodeFuse-CodeLlama-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/CodeFuse-CodeLlama-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codefuse-ai/CodeFuse-CodeLlama-34B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/CodeFuse-CodeLlama-34B") model = AutoModelForCausalLM.from_pretrained("codefuse-ai/CodeFuse-CodeLlama-34B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codefuse-ai/CodeFuse-CodeLlama-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codefuse-ai/CodeFuse-CodeLlama-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codefuse-ai/CodeFuse-CodeLlama-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codefuse-ai/CodeFuse-CodeLlama-34B
- SGLang
How to use codefuse-ai/CodeFuse-CodeLlama-34B 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 "codefuse-ai/CodeFuse-CodeLlama-34B" \ --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": "codefuse-ai/CodeFuse-CodeLlama-34B", "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 "codefuse-ai/CodeFuse-CodeLlama-34B" \ --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": "codefuse-ai/CodeFuse-CodeLlama-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use codefuse-ai/CodeFuse-CodeLlama-34B with Docker Model Runner:
docker model run hf.co/codefuse-ai/CodeFuse-CodeLlama-34B
Output (Python code) needs to be improved
['Here is a Python function for quick sort:\n\n\npython\ndef quicksort(arr):\n if len(arr) <= 1:\n return arr\n else:\n pivot = arr[len(arr) // 2]\n less = [x for x in arr if x < pivot]\n equal = [x for x in arr if x == pivot]\n greater = [x for x in arr if x > pivot]\n return quicksort(less) + equal + quicksort(greater)\n\n\n\nThis function works by choosing a pivot element from the array (in this case, the middle element), and partitioning the other elements into two sub-arrays: one where all elements are less than the pivot, and one where all elements are greater than or equal to the pivot. It then recursively sorts the sub-arrays. This process continues until the base case of an array of length 0 or 1 is reached, at which point the function returns the array.']