Instructions to use codellama/CodeLlama-7b-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codellama/CodeLlama-7b-hf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="codellama/CodeLlama-7b-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf")
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codellama/CodeLlama-7b-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-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-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/codellama/CodeLlama-7b-hf

SGLang

How to use codellama/CodeLlama-7b-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-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-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-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-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codellama/CodeLlama-7b-hf with Docker Model Runner:
```
docker model run hf.co/codellama/CodeLlama-7b-hf
```

osanseviero commited on Aug 24, 2023

Commit

7e58ad0

1 Parent(s): bca487f

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -1

README.md CHANGED Viewed

@@ -13,7 +13,15 @@ Code Llama is a collection of pretrained and fine-tuned generative text models r
 **Model Developers** Meta
-**Variations** Code Llama comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
 **Input** Models input text only.

 **Model Developers** Meta
+**Variations** Code Llama comes in three model sizes, and three variants:
+* Code Llama: base models designed for general code synthesis and understanding
+* Code Llama - Python: designed specifically for Python
+* Code Llama - Instruct: for instruction following and safer deployment
+* All variants are available in sizes of 7B, 13B and 34B parameters.
+**This repository contains the base model of 7B parameters.**
 **Input** Models input text only.