BitNet-b1.58-2B-4T
Microsoft’s 2-billion-parameter 1-bit language model trained on four trillion tokens
BitNet-b1.58-2B-4T is a flagship checkpoint in the BitNet family. The name encodes: b1.58 (ternary-style 1.58-bit weight encoding used in the BitNet line), 2B parameters, and 4T tokens of training data. It is distributed in GGUF form for use with the BitNet inference codebase (and compatible tooling).
Who should use this model?
Choose BitNet-b1.58-2B-4T when you want a balance of capability and footprint: smaller than 7B–8B models but stronger than tiny sub-1B models for many tasks. It pairs well with local inference experiments and prototypes.
Download
Official GGUF artifacts are published on Hugging Face (e.g. microsoft/BitNet-b1.58-2B-4T-gguf). See Download BitNet models from Hugging Face for CLI examples and all supported models for siblings like 3B and Falcon variants.
Running with BitNet
After installation, point setup_env.py and run_inference.py at your local GGUF path. Quantization flags (such as i2_s) must match your file; follow the quick start verbatim first, then customize prompts and context in usage.
Quality vs other sizes
Larger BitNet-compatible models (e.g. 8B-class) can improve fluency and knowledge at higher RAM cost. Compare trade-offs in LLM memory comparison and BitNet vs other quantization.