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

Libraries

How to use mzbac/CodeLlama-34b-guanaco with Transformers:

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

pipe = pipeline("text-generation", model="mzbac/CodeLlama-34b-guanaco")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mzbac/CodeLlama-34b-guanaco")
model = AutoModelForCausalLM.from_pretrained("mzbac/CodeLlama-34b-guanaco")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mzbac/CodeLlama-34b-guanaco with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mzbac/CodeLlama-34b-guanaco"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mzbac/CodeLlama-34b-guanaco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mzbac/CodeLlama-34b-guanaco

SGLang

How to use mzbac/CodeLlama-34b-guanaco 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 "mzbac/CodeLlama-34b-guanaco" \
    --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": "mzbac/CodeLlama-34b-guanaco",
		"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 "mzbac/CodeLlama-34b-guanaco" \
        --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": "mzbac/CodeLlama-34b-guanaco",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Codellama 34b base model fine-tuned on the text chunk from the OpenAssistant-Guanaco dataset instead of Q&A pair, so it struggles to determine the end of the answer. recommend using a stop string like "### Human:" to prevent the model from talking to itself.

Prompt template:

### Human: {prompt}
### Assistant:

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