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 Settings
- 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
Why are these models fp32?
It is glad to see interest is growing in 1bit LLMs but why are your models fp32? Did hugging face converted them to make them compatible with transformers library? How to use original 1bit models? What is the size and speed difference from fp32?
yeah it's weird, they could have used FP8 instead
Maybe it miscategorized it? Seems smaller than similar fp16 model
Maybe it miscategorized it? Seems smaller than similar fp16 model
Gemma 2.8b with fp16 is almost 5GB, while this is 3B and is 13.3GB so, I don't think it is fp16 let alone 1bit.
Ah somehow misremembering the file size sorry, not sure how that happened tbh
Yes, it seems the weight are not ternary {-1 0, 1}, instead it is float32. Even if weight datatype is float32, at least they had to make weight ternary {-1, 0, 1} so that we can see the performace of it when it really uses 1, -1, 0 in weights