Text Generation
Transformers
Safetensors
PyTorch
English
llama
facebook
meta
llama-2
text-generation-inference
4-bit precision
gptq
Instructions to use TheBloke/Llama-2-13B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Llama-2-13B-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/Llama-2-13B-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-13B-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-13B-GPTQ", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/Llama-2-13B-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/Llama-2-13B-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/Llama-2-13B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/Llama-2-13B-GPTQ
- SGLang
How to use TheBloke/Llama-2-13B-GPTQ 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/Llama-2-13B-GPTQ" \ --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/Llama-2-13B-GPTQ", "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/Llama-2-13B-GPTQ" \ --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/Llama-2-13B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/Llama-2-13B-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/Llama-2-13B-GPTQ
- Xet hash:
- bd7f1d54fdb40aeb2d6a542bd60f0d25709f1f219e0f3e54ceb70e24b786f7c2
- Size of remote file:
- 7.26 GB
- SHA256:
- 924e8df9a78b0293d3e67992fc3a032981a707afb456fbd4de22227a00baa11b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.