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

Libraries

How to use 1bitLLM/bitnet_b1_58-large with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="1bitLLM/bitnet_b1_58-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("1bitLLM/bitnet_b1_58-large")
model = AutoModelForCausalLM.from_pretrained("1bitLLM/bitnet_b1_58-large")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 1bitLLM/bitnet_b1_58-large 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-large"
# 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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/1bitLLM/bitnet_b1_58-large

SGLang

How to use 1bitLLM/bitnet_b1_58-large 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-large" \
    --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-large",
		"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-large" \
        --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-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 1bitLLM/bitnet_b1_58-large with Docker Model Runner:
```
docker model run hf.co/1bitLLM/bitnet_b1_58-large
```

Is it fair to say...

by stefanreich - opened Dec 21, 2025

Discussion

stefanreich

Dec 21, 2025

...that this model is kind of... very bad? :) Like not realistically usable? Just checking my sanity here.

python3 run_inference.py -m models/bitnet-large/model.gguf -p "The capital of France is" 2>/dev/null
The capital of France is Paris, which is the most popular city in France and one of the most visited cities in Europe. It is located in the south east part of France and is the most popular place of visit in France. It is one of the most popular cities for tourists in France.
Paris is the capital and most populated city of France. It is one of the biggest cities in Europe and one of the most visited cities in the world. The French capital is famous for its beautiful architecture, its magnificent monuments and the beautiful streetscapes. The capital is also famous for its restaurants, fashion shops, cafes and famous

stefanreich

Dec 21, 2025

I did convert to gguf so maybe I did something wrong?

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