Instructions to use codellama/CodeLlama-34b-Instruct-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codellama/CodeLlama-34b-Instruct-hf with Transformers:

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

pipe = pipeline("text-generation", model="codellama/CodeLlama-34b-Instruct-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-34b-Instruct-hf")
model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-34b-Instruct-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codellama/CodeLlama-34b-Instruct-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codellama/CodeLlama-34b-Instruct-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codellama/CodeLlama-34b-Instruct-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codellama/CodeLlama-34b-Instruct-hf

SGLang

How to use codellama/CodeLlama-34b-Instruct-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-34b-Instruct-hf" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codellama/CodeLlama-34b-Instruct-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-34b-Instruct-hf" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codellama/CodeLlama-34b-Instruct-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use codellama/CodeLlama-34b-Instruct-hf with Docker Model Runner:
```
docker model run hf.co/codellama/CodeLlama-34b-Instruct-hf
```

Best codeLlama model for query SQL generation

#24

by matteon - opened Feb 7, 2024

Discussion

matteon

Feb 7, 2024

I have to choose the best model to generate sql code from natural language.
When I talk about the best model I mean the one that can best understand how to use sql operators like 'like' and that can be accurate even with rather long conditions.
Which one would you recommend between : codellama/CodeLlama-13b-Instruct-hf and codellama/CodeLlama-13b-hf (also considering 34b versions of parameters) ?

faroncoder

Feb 24, 2024

“Instruct” typically refers to providing guidance, directions, or explanations on how to perform a task you want AI to do whereas “chat” implies engaging in conversation or dialogue with someone.

If you are comfortable working with SQL commands (programmer, or such); for software or documentation, “instruct” is what you would use to explain how to do the tasks, and AI learns from you and perform tasks as you told it to, while you expect AI to already “know” these SQLs.

If you are not programmer and have basic knowledge how to do SQLs, then, chat is probably is one to use as chat can “share” with you the knowledge how to use SQL.

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