Instructions to use microsoft/bitnet-b1.58-2B-4T-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/bitnet-b1.58-2B-4T-gguf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/bitnet-b1.58-2B-4T-gguf") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("microsoft/bitnet-b1.58-2B-4T-gguf", dtype="auto") - llama-cpp-python
How to use microsoft/bitnet-b1.58-2B-4T-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="microsoft/bitnet-b1.58-2B-4T-gguf", filename="ggml-model-i2_s.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use microsoft/bitnet-b1.58-2B-4T-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf microsoft/bitnet-b1.58-2B-4T-gguf # Run inference directly in the terminal: llama-cli -hf microsoft/bitnet-b1.58-2B-4T-gguf
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf microsoft/bitnet-b1.58-2B-4T-gguf # Run inference directly in the terminal: llama-cli -hf microsoft/bitnet-b1.58-2B-4T-gguf
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf microsoft/bitnet-b1.58-2B-4T-gguf # Run inference directly in the terminal: ./llama-cli -hf microsoft/bitnet-b1.58-2B-4T-gguf
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf microsoft/bitnet-b1.58-2B-4T-gguf # Run inference directly in the terminal: ./build/bin/llama-cli -hf microsoft/bitnet-b1.58-2B-4T-gguf
Use Docker
docker model run hf.co/microsoft/bitnet-b1.58-2B-4T-gguf
- LM Studio
- Jan
- vLLM
How to use microsoft/bitnet-b1.58-2B-4T-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/bitnet-b1.58-2B-4T-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/bitnet-b1.58-2B-4T-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/microsoft/bitnet-b1.58-2B-4T-gguf
- SGLang
How to use microsoft/bitnet-b1.58-2B-4T-gguf 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 "microsoft/bitnet-b1.58-2B-4T-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/bitnet-b1.58-2B-4T-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "microsoft/bitnet-b1.58-2B-4T-gguf" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/bitnet-b1.58-2B-4T-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use microsoft/bitnet-b1.58-2B-4T-gguf with Ollama:
ollama run hf.co/microsoft/bitnet-b1.58-2B-4T-gguf
- Unsloth Studio
How to use microsoft/bitnet-b1.58-2B-4T-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/bitnet-b1.58-2B-4T-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for microsoft/bitnet-b1.58-2B-4T-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for microsoft/bitnet-b1.58-2B-4T-gguf to start chatting
- Docker Model Runner
How to use microsoft/bitnet-b1.58-2B-4T-gguf with Docker Model Runner:
docker model run hf.co/microsoft/bitnet-b1.58-2B-4T-gguf
- Lemonade
How to use microsoft/bitnet-b1.58-2B-4T-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull microsoft/bitnet-b1.58-2B-4T-gguf
Run and chat with the model
lemonade run user.bitnet-b1.58-2B-4T-gguf-{{QUANT_TAG}}List all available models
lemonade list
Data Summary for microsoft_bitnet-b1.58-2B-4T, bitnet-b1.58-2B-4T-gguf, bitnet-b1.58-2B-4T-bf16
1. General information
1.0.1 Version of the Summary: 1.0
1.0.2 Last update: 16-Dec-2025
1.1 Model Developer Identification
1.1.1 Model Developer name and contact details: Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080.
1.2 Model Identification
1.2.1 Versioned model name(s): bitnet-b1.58-2B-4T
1.2.2 Model release date: 01-May-2025
1.3 Overall training data size and characteristics
1.3.1 Size of dataset and characteristics
1.3.1.A Text training data size: 1 billion to 10 trillion tokens
1.3.1.B Text training data content: We used SmolLM-Corpus,dclm-baseline-1.0 and open-web-math datasets to train this model.
1.3.1.C Image training data size: Not applicable. Images are not part of the training data
1.3.1.D Image training data content: Not applicable
1.3.1.E Audio training data size: Not applicable. Audio content is not part of the training data
1.3.1.F Audio training data content: Not applicable
1.3.1.G Video training data size: Not applicable. Videos are not part of the training data
1.3.1.H Video training data content: Not applicable
1.3.1.I Other training data size: Not applicable
1.3.1.J Other training data content: Not applicable
1.3.2 Latest date of data acquisition/collection for model training: 15-Jul-2024
1.3.3 Is data collection ongoing to update the model with new data collection after deployment? No
1.3.4 Date the training dataset was first used to train the model: 12-Jan-2025
1.3.5 Rationale or purpose of data selection: The training corpus included publicly available text and code datasets to provide broad world knowledge and foundational language capabilities, with synthetic mathematical data to enhance reasoning. Selected datasets were emphasized later to refine performance
2. List of data sources
2.1 Publicly available datasets
2.1.1 Have you used publicly available datasets to train the model? Yes
2.2 Private non-publicly available datasets obtained from third parties
2.2.1 Datasets commercially licensed by rights holders or their representatives
2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives? No
2.2.2 Private datasets obtained from other third-parties
2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries? No
2.3 Personal Information
2.3.1 Was personal data used to train the model? Microsoft follows all relevant laws and regulations pertaining to personal information
2.4 Synthetic data
2.4.1 Was any synthetic AI-generated data used to train the model? No
3. Data processing aspects
3.1 Respect of reservation of rights from text and data mining exception or limitation
3.1.1 Does this dataset include any data protected by copyright, trademark, or patent? Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent
3.2 Other information
3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities? Microsoft follows all required regulations and laws for protecting consumer identities
3.2.2 Was the dataset cleaned or modified before model training? Yes