Instructions to use 1bitLLM/bitnet_b1_58-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 1bitLLM/bitnet_b1_58-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="1bitLLM/bitnet_b1_58-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("1bitLLM/bitnet_b1_58-3B") model = AutoModelForCausalLM.from_pretrained("1bitLLM/bitnet_b1_58-3B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use 1bitLLM/bitnet_b1_58-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "1bitLLM/bitnet_b1_58-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "1bitLLM/bitnet_b1_58-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/1bitLLM/bitnet_b1_58-3B
- SGLang
How to use 1bitLLM/bitnet_b1_58-3B 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 "1bitLLM/bitnet_b1_58-3B" \ --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": "1bitLLM/bitnet_b1_58-3B", "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 "1bitLLM/bitnet_b1_58-3B" \ --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": "1bitLLM/bitnet_b1_58-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use 1bitLLM/bitnet_b1_58-3B with Docker Model Runner:
docker model run hf.co/1bitLLM/bitnet_b1_58-3B
Longer inference time
#4
by dittops - opened
Inference time seems higher than a normal fp16 model. I was expecting better throughput as the advantage of 1bit models
The advantage of 1 bit models is that they are 32 smaller compared ro 32 bit model. The inference on 1 bit models includes the overhead of dequantization.
However, as per the paper, there is a significant improvement in memory and throughput.