Instructions to use 1bitLLM/bitnet_b1_58-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a reproduction of the BitNet b1.58 paper. The models are trained with RedPajama dataset for 100B tokens. The hypers, as well as two-stage LR and weight decay, are implemented as suggested in their following paper. All models are open-source in the repo. We will train larger models and/or more tokens when resource is available.

Results

PPL and zero-shot accuracy:

Models	PPL	ARCe	ARCc	HS	BQ	OQ	PQ	WGe	Avg
FP16 700M (reported)	12.33	54.7	23.0	37.0	60.0	20.2	68.9	54.8	45.5
BitNet b1.58 700M (reported)	12.87	51.8	21.4	35.1	58.2	20.0	68.1	55.2	44.3
BitNet b1.58 700M (reproduced)	12.78	51.4	21.8	35.0	59.6	20.6	67.5	55.4	44.5
FP16 1.3B (reported)	11.25	56.9	23.5	38.5	59.1	21.6	70.0	53.9	46.2
BitNet b1.58 1.3B (reported)	11.29	54.9	24.2	37.7	56.7	19.6	68.8	55.8	45.4
BitNet b1.58 1.3B (reproduced)	11.19	55.8	23.7	37.6	59.0	20.2	69.2	56.0	45.9
FP16 3B (reported)	10.04	62.1	25.6	43.3	61.8	24.6	72.1	58.2	49.7
BitNet b1.58 3B (reported)	9.91	61.4	28.3	42.9	61.5	26.6	71.5	59.3	50.2
BitNet b1.58 3B (reproduced)	9.88	60.9	28.0	42.3	58.3	26.0	71.4	60.3	49.6

The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors.

Evaluation

The evaluation pipelines are from the paper authors. Here is the commands to run the evaluation:

pip install lm-eval==0.3.0

python eval_ppl.py --hf_path 1bitLLM/bitnet_b1_58-3B --seqlen 2048

python eval_task.py --hf_path 1bitLLM/bitnet_b1_58-3B \
    --batch_size 1 \
    --tasks \
    --output_path result.json \
    --num_fewshot 0 \
    --ctx_size 2048