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

Libraries

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

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

pipe = pipeline("text-generation", model="prem-research/CodeLlama-34b-Instruct-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prem-research/CodeLlama-34b-Instruct-hf")
model = AutoModelForCausalLM.from_pretrained("prem-research/CodeLlama-34b-Instruct-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prem-research/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 "prem-research/CodeLlama-34b-Instruct-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prem-research/CodeLlama-34b-Instruct-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use prem-research/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 "prem-research/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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prem-research/CodeLlama-34b-Instruct-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 "prem-research/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/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prem-research/CodeLlama-34b-Instruct-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

filopedraz commited on Sep 5, 2023

Commit

da062c1

1 Parent(s): 59358fd

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@@ -8,7 +8,7 @@ license: llama2
 ---
 # Code Llama for Petals
-Resharded Code Llama repository optimized for Petals inference. Instead of having 7 of ~10GiB each, the current repository has 49 shards each one of ~1.5GiB.
 For more information about the model, you can check the official card [here](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf).

 ---
 # Code Llama for Petals
+Resharded Code Llama repository optimized for Petals inference. Instead of having 7 shards of ~10GiB each, the current repository has 49 shards each one of ~1.5GiB.
 For more information about the model, you can check the official card [here](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf).