Instructions to use TinyPixel/CodeLlama-7B-Python-bf16-sharded with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TinyPixel/CodeLlama-7B-Python-bf16-sharded with Transformers:

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

pipe = pipeline("text-generation", model="TinyPixel/CodeLlama-7B-Python-bf16-sharded", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TinyPixel/CodeLlama-7B-Python-bf16-sharded", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TinyPixel/CodeLlama-7B-Python-bf16-sharded", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TinyPixel/CodeLlama-7B-Python-bf16-sharded with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TinyPixel/CodeLlama-7B-Python-bf16-sharded"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TinyPixel/CodeLlama-7B-Python-bf16-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TinyPixel/CodeLlama-7B-Python-bf16-sharded

SGLang

How to use TinyPixel/CodeLlama-7B-Python-bf16-sharded 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 "TinyPixel/CodeLlama-7B-Python-bf16-sharded" \
    --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": "TinyPixel/CodeLlama-7B-Python-bf16-sharded",
		"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 "TinyPixel/CodeLlama-7B-Python-bf16-sharded" \
        --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": "TinyPixel/CodeLlama-7B-Python-bf16-sharded",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TinyPixel/CodeLlama-7B-Python-bf16-sharded with Docker Model Runner:
```
docker model run hf.co/TinyPixel/CodeLlama-7B-Python-bf16-sharded
```

CodeLlama-7B-Python-bf16-sharded / pytorch_model-00004-of-00007.bin

TinyPixel

Upload LlamaForCausalLM

48da1f3 about 3 years ago

History Blame Contribute Delete

1.99 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: a5bbfa022bdbbc0695f431297216de68c4eb77c4ddaaf30399e7d5615157364d
Size of remote file:: 1.99 GB
SHA256:: a43315d33d563790bb0daaa77128b775b13f754cfda1eedea71eae21dcb5df28

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

TinyPixel
/

CodeLlama-7B-Python-bf16-sharded

Detected Pickle imports (3)

Xet Pointer Details