Text Generation
Transformers
Safetensors
code
llama
llama-2
text-generation-inference
4-bit precision
awq
Instructions to use TheBloke/CodeLlama-13B-Python-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/CodeLlama-13B-Python-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/CodeLlama-13B-Python-AWQ")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/CodeLlama-13B-Python-AWQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-13B-Python-AWQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/CodeLlama-13B-Python-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/CodeLlama-13B-Python-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/CodeLlama-13B-Python-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/CodeLlama-13B-Python-AWQ
- SGLang
How to use TheBloke/CodeLlama-13B-Python-AWQ 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 "TheBloke/CodeLlama-13B-Python-AWQ" \ --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": "TheBloke/CodeLlama-13B-Python-AWQ", "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 "TheBloke/CodeLlama-13B-Python-AWQ" \ --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": "TheBloke/CodeLlama-13B-Python-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/CodeLlama-13B-Python-AWQ with Docker Model Runner:
docker model run hf.co/TheBloke/CodeLlama-13B-Python-AWQ
Commit History
-
Update for Transformers AWQ support
b703a8e
-
Update base_model formatting
702cebe
-
Upload README.md
68acbe3 -
Upload README.md
7f06f1b -
Upload README.md
61ad2a5 -
Upload README.md
3ca1790 -
Upload README.md
5833590 -
Upload README.md
dac51d0 -
Upload README.md
b2a3165 -
Upload README.md
bbaaa8b -
AWQ model commit
9735f27 -
AWQ model commit
82b4dc9 -
AWQ model commit
478d0da -
AWQ model commit
3167935 -
AWQ model commit
73ea958 -
AWQ model commit
0d62948 -
AWQ model commit
10ae360 -
AWQ model commit
fe82b21 -
AWQ model commit
4b2e151 -
AWQ model commit
c36452b -
AWQ model commit
66fc3ca -
AWQ model commit
ca1759b -
AWQ model commit
1848924
-
initial commit
c5b028b