Instructions to use catid/llama-65b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use catid/llama-65b-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="catid/llama-65b-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("catid/llama-65b-4bit") model = AutoModelForCausalLM.from_pretrained("catid/llama-65b-4bit") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use catid/llama-65b-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "catid/llama-65b-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "catid/llama-65b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/catid/llama-65b-4bit
- SGLang
How to use catid/llama-65b-4bit 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 "catid/llama-65b-4bit" \ --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": "catid/llama-65b-4bit", "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 "catid/llama-65b-4bit" \ --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": "catid/llama-65b-4bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use catid/llama-65b-4bit with Docker Model Runner:
docker model run hf.co/catid/llama-65b-4bit
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Check out the documentation for more information.
llama-65b-4bit
This works with my branch of GPTQ-for-LLaMa: https://github.com/catid/GPTQ-for-LLaMa-65B-2GPU
To test it out on two RTX4090 GPUs and 64GB RAM (might work with a big swap file haven't tested):
# Install git-lfs
sudo apt install git git-lfs
# Clone the code
git clone https://github.com/catid/GPTQ-for-LLaMa-65B-2GPU
cd GPTQ-for-LLaMa-65B-2GPU
# Clone the model weights
git lfs install
git clone https://huggingface.co/catid/llama-65b-4bit
# Set up conda environment
conda create -n gptq python=3.10
conda activate gptq
# Install script dependencies
pip install -r requirements.txt
# Work around protobuf error
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
# Run a test
python llama_inference.py llama-65b-4bit --load llama-65b-4bit/llama65b-4bit-128g.safetensors --groupsize 128 --wbits 4 --text "I woke up with a dent in my forehead. " --max_length 128 --min_length 32
license: bsd-3-clause
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