Instructions to use tzervas/lab-bitnet-b1.58-2B-4T-i2s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tzervas/lab-bitnet-b1.58-2B-4T-i2s with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s # Run inference directly in the terminal: llama cli -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s # Run inference directly in the terminal: llama cli -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s
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 tzervas/lab-bitnet-b1.58-2B-4T-i2s # Run inference directly in the terminal: ./llama-cli -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s
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 tzervas/lab-bitnet-b1.58-2B-4T-i2s # Run inference directly in the terminal: ./build/bin/llama-cli -hf tzervas/lab-bitnet-b1.58-2B-4T-i2s
Use Docker
docker model run hf.co/tzervas/lab-bitnet-b1.58-2B-4T-i2s
- LM Studio
- Jan
- Ollama
How to use tzervas/lab-bitnet-b1.58-2B-4T-i2s with Ollama:
ollama run hf.co/tzervas/lab-bitnet-b1.58-2B-4T-i2s
- Unsloth Desktop
- Docker Model Runner
How to use tzervas/lab-bitnet-b1.58-2B-4T-i2s with Docker Model Runner:
docker model run hf.co/tzervas/lab-bitnet-b1.58-2B-4T-i2s
- Lemonade
How to use tzervas/lab-bitnet-b1.58-2B-4T-i2s with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tzervas/lab-bitnet-b1.58-2B-4T-i2s
Run and chat with the model
lemonade run user.lab-bitnet-b1.58-2B-4T-i2s-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
lab-bitnet BitNet-b1.58-2B-4T I2_S
Official Microsoft b1.58 I2_S GGUF (ggml-model-i2_s.gguf), not GGUF Q4.
Card targets: gpu5080 sm120, akula-prime sm86, gpu-1080ti sm61 (file is ~1.2 GB).
Layer-group ternary convert (scripts/layer_group_quant.py) checkpointed on gpu5080 against the bf16 safetensors.
See ggml-model-i2_s.quant.json.
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Hardware compatibility
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