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
Can you provide more details on the training?
I try to reproduce the result on 700M bitnet b1.58b model, but I failed.
Instead of being S-shaped, the loss curve showed an exponential decay. The ppl of the final model was 18.7, but in the paper it was 12.87, and the 700M model you provided was also achievable.
I think my training setup is exactly the same as the paper, but I don't know exactly how the training set RedPajama-100B was generated, I need more details about the training and dataset
I have met the same problem, my loss curve declined quickly when training a 1.1B model, and the MFU is very low.