mirror of
https://github.com/microsoft/BitNet.git
synced 2026-10-07 23:31:19 +00:00
428 lines
20 KiB
Markdown
428 lines
20 KiB
Markdown
<div align="center">
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# bitnet.cpp
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[](https://opensource.org/licenses/MIT)
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[](https://huggingface.co/collections/microsoft/bitnet)
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[](https://arxiv.org/abs/2502.11880)
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[](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/)
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[](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)
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</div>
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<div align="left">
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<h3>📰 News</h3>
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<strong>07/23/2026:</strong> 📣 We released <a href="https://github.com/microsoft/VibeASR.cpp"><strong>VibeASR.cpp</strong></a> — a real-time multilingual ASR inference engine on CPU using BitNet I2_S quantization, achieving RTF < 1 with very few threads on x86 (AVX2) and ARM (NEON) platforms. [<a href="https://github.com/microsoft/VibeASR.cpp">Code</a>] [<a href="https://huggingface.co/microsoft/VibeVoice-ASR-BitNet">Models</a>] [<a href="https://arxiv.org/abs/2607.21075">Report</a>] 
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<strong>07/20/2026:</strong> 📣 We released <a href="https://huggingface.co/microsoft/BitNet-embedding-0.6B"><strong>BitNet-embedding-0.6B</strong></a> and <a href="https://huggingface.co/microsoft/BitNet-embedding-270M"><strong>BitNet-embedding-270M</strong></a> on Hugging Face — the first 1-bit embedding models that deliver competitive embedding quality with significantly faster inference on CPUs.
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- **1.42x to 2.28x speedup** over F16 on BitNet-embedding-0.6B prefill (8 threads)
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- **1.32x to 1.74x speedup** over F16 on BitNet-embedding-270M prefill (8 threads)
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- Supports I2_S conversion with optimized kernels on x86 CPUs
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- Lossless inference with 2 bits per weight
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07/16/2026: 📣 Released [BitNet Embeddings 0.6B/270M: I2_S Conversion and Inference Optimization](docs/bitnet-embeddings-i2s-guide.md) — detailed guide for converting and running BitNet embedding models with optimized I2_S kernels.
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01/15/2026: 📣 Released [BitNet CPU Inference Optimization](https://github.com/microsoft/BitNet/blob/main/src/README.md) — parallel kernel implementations with configurable tiling and embedding quantization support, achieving **1.15x to 2.1x** additional speedup over the original implementation.
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05/20/2025: 📣 Released [BitNet Official GPU inference kernel](https://github.com/microsoft/BitNet/blob/main/gpu/README.md) — extending 1-bit inference beyond CPUs.
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04/14/2025: 📣 Released [BitNet Official 2B Parameter Model](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) on Hugging Face — the first official BitNet b1.58 model trained with 4T tokens.
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02/18/2025: 📑 [Bitnet.cpp: Efficient Edge Inference for Ternary LLMs](https://arxiv.org/abs/2502.11880) — system-level paper on bitnet.cpp's architecture and design.
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11/08/2024: 📑 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965) — enabling 4-bit activations for further efficiency gains.
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10/21/2024: 📑 [1-bit AI Infra: Part 1.1, Fast and Lossless BitNet b1.58 Inference on CPUs](https://arxiv.org/abs/2410.16144) — the technical report behind bitnet.cpp.
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10/17/2024: 📣 bitnet.cpp 1.0 released.
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03/21/2024: 📑 [The-Era-of-1-bit-LLMs: Training Tips, Code, FAQ](https://github.com/microsoft/unilm/blob/master/bitnet/The-Era-of-1-bit-LLMs__Training_Tips_Code_FAQ.pdf)
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02/27/2024: 📑 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764) — the foundational paper introducing BitNet b1.58.
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10/17/2023: 📑 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453) — the original BitNet paper.
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</div>
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## Overview
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bitnet.cpp is the official inference framework for 1-bit LLMs (e.g., BitNet b1.58). It offers a suite of optimized kernels that support **fast** and **lossless** inference of 1.58-bit models on **CPU** and **GPU** (NPU support coming next).
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Try it out via this [online demo](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/), or build and run it on your own [CPU](https://github.com/microsoft/BitNet?tab=readme-ov-file#build-from-source) or [GPU](https://github.com/microsoft/BitNet/blob/main/gpu/README.md).
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bitnet.cpp achieves speedups of **1.37x** to **5.07x** on ARM CPUs, with larger models experiencing greater performance gains. Additionally, it reduces energy consumption by **55.4%** to **70.0%**, further boosting overall efficiency. On x86 CPUs, speedups range from **2.37x** to **6.17x** with energy reductions between **71.9%** to **82.2%**. Furthermore, bitnet.cpp can run a 100B BitNet b1.58 model on a single CPU, achieving speeds comparable to human reading (5-7 tokens per second), significantly enhancing the potential for running LLMs on local devices. Please refer to the [technical report](https://arxiv.org/abs/2410.16144) for more details.
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<img src="./assets/performance.png" alt="performance_comparison" width="800"/>
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## Model Releases
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### 1. [BitNet-b1.58-2B-4T](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) - 1-bit Large Language Model
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**BitNet-b1.58-2B-4T** is the first official BitNet b1.58 model with **2.4B parameters**, trained on **4 trillion tokens**. It is a ternary (1.58-bit) language model that delivers competitive performance with full-precision models of similar size while enabling significantly faster and more energy-efficient inference.
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- **Fast CPU Inference**: Achieves up to **6.17x speedup** on x86 CPUs and **5.07x** on ARM CPUs compared to full-precision models.
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- **Energy Efficient**: Reduces energy consumption by up to **82.2%** on x86 and **70.0%** on ARM.
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- **GPU Support**: Official GPU inference kernel available for accelerated deployment.
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- **Chat-Ready**: Supports conversational mode for interactive use.
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[🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) | [🔗 Online Demo](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/) | [📄 Technical Report](https://arxiv.org/abs/2410.16144)
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<img src="./assets/bitnet_b1.58_2b_benchmark.png" alt="BitNet b1.58 2B Benchmark" width="600"/>
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### 2. [BitNet-embedding-0.6B](https://huggingface.co/microsoft/BitNet-embedding-0.6B) - 1-bit Embedding Model
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**BitNet-embedding-0.6B** is a **0.6B-parameter** 1-bit embedding model that achieves competitive embedding quality with significantly faster CPU inference. It is the first model to demonstrate that ternary weights can deliver strong performance on embedding tasks.
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- **1.42x to 2.28x speedup** over F16 on prefill (8 threads, x86)
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- **Lossless Quality**: Competitive embedding quality with 2 bits per weight
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- **I2_S Kernel**: Supports optimized I2_S conversion on x86 CPUs
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[🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-0.6B) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md)
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<img src="./assets/embedding_prefill_0.6B.png" alt="BitNet Embedding 0.6B Prefill Performance" width="600"/>
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### 3. [BitNet-embedding-270M](https://huggingface.co/microsoft/BitNet-embedding-270M) - Lightweight 1-bit Embedding Model
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**BitNet-embedding-270M** is a compact **270M-parameter** 1-bit embedding model designed for resource-constrained environments, offering fast inference with minimal memory footprint.
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- **1.32x to 1.74x speedup** over F16 on prefill (8 threads, x86)
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- **Lossless Quality**: Competitive embedding quality with 2 bits per weight
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- **Lightweight**: Only 270M parameters for edge deployment scenarios
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[🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-270M) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md)
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<img src="./assets/embedding_prefill_270M.png" alt="BitNet Embedding 270M Prefill Performance" width="600"/>
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## Supported Models
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<table>
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<tr>
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<th rowspan="2">Model</th>
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<th rowspan="2">Parameters</th>
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<th rowspan="2">CPU</th>
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<th colspan="3">Kernel</th>
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</tr>
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<tr>
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<th>I2_S</th>
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<th>TL1</th>
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<th>TL2</th>
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</tr>
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<tr>
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<th colspan="6" style="text-align:left;">Official Models</th>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/microsoft/BitNet-b1.58-2B-4T">BitNet-b1.58-2B-4T</a></td>
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<td rowspan="2">2.4B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>✅</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/microsoft/BitNet-embedding-0.6B">BitNet-embedding-0.6B</a></td>
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<td rowspan="2">0.6B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>❌</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>❌</td>
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<td>❌</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/microsoft/BitNet-embedding-270M">BitNet-embedding-270M</a></td>
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<td rowspan="2">270M</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>❌</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>❌</td>
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<td>❌</td>
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<td>❌</td>
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</tr>
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<tr>
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<th colspan="6" style="text-align:left;">Community Models</th>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/1bitLLM/bitnet_b1_58-large">bitnet_b1_58-large</a></td>
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<td rowspan="2">0.7B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>✅</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/1bitLLM/bitnet_b1_58-3B">bitnet_b1_58-3B</a></td>
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<td rowspan="2">3.3B</td>
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<td>x86</td>
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<td>❌</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>❌</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/HF1BitLLM/Llama3-8B-1.58-100B-tokens">Llama3-8B-1.58-100B-tokens</a></td>
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<td rowspan="2">8.0B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>✅</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/collections/tiiuae/falcon3-67605ae03578be86e4e87026">Falcon3 Family</a></td>
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<td rowspan="2">1B-10B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>✅</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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<tr>
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<td rowspan="2"><a href="https://huggingface.co/collections/tiiuae/falcon-edge-series-6804fd13344d6d8a8fa71130">Falcon-E Family</a></td>
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<td rowspan="2">1B-3B</td>
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<td>x86</td>
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<td>✅</td>
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<td>❌</td>
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<td>✅</td>
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</tr>
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<tr>
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<td>ARM</td>
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<td>✅</td>
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<td>✅</td>
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<td>❌</td>
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</tr>
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</table>
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❗️**We use existing 1-bit LLMs available on [Hugging Face](https://huggingface.co/) to demonstrate the inference capabilities of bitnet.cpp. We hope the release of bitnet.cpp will inspire the development of 1-bit LLMs in large-scale settings in terms of model size and training tokens.**
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## Installation
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### Requirements
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- python>=3.10
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- cmake>=3.22
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- clang>=18
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- For Windows users, install [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/). In the installer, toggle on at least the following options(this also automatically installs the required additional tools like CMake):
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- Desktop-development with C++
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- C++-CMake Tools for Windows
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- Git for Windows
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- C++-Clang Compiler for Windows
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- MS-Build Support for LLVM-Toolset (clang)
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- For Debian/Ubuntu users, you can download with [Automatic installation script](https://apt.llvm.org/)
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`bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)"`
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- conda (highly recommend)
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### Build from source
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> [!IMPORTANT]
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> If you are using Windows, please remember to always use a Developer Command Prompt / PowerShell for VS2022 for the following commands. Please refer to the FAQs below if you see any issues.
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1. Clone the repo
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```bash
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git clone --recursive https://github.com/microsoft/BitNet.git
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cd BitNet
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```
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2. Install the dependencies
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```bash
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# (Recommended) Create a new conda environment
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conda create -n bitnet-cpp python=3.10
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conda activate bitnet-cpp
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pip install -r requirements.txt
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```
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3. Build the project
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```bash
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# Manually download the model and run with local path
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huggingface-cli download microsoft/BitNet-b1.58-2B-4T-gguf --local-dir models/BitNet-b1.58-2B-4T
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python setup_env.py -md models/BitNet-b1.58-2B-4T -q i2_s
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```
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<pre>
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usage: setup_env.py [-h] [--hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}] [--model-dir MODEL_DIR] [--log-dir LOG_DIR] [--quant-type {i2_s,tl1}] [--quant-embd]
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[--use-pretuned]
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Setup the environment for running inference
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optional arguments:
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-h, --help show this help message and exit
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--hf-repo {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}, -hr {1bitLLM/bitnet_b1_58-large,1bitLLM/bitnet_b1_58-3B,HF1BitLLM/Llama3-8B-1.58-100B-tokens,tiiuae/Falcon3-1B-Instruct-1.58bit,tiiuae/Falcon3-3B-Instruct-1.58bit,tiiuae/Falcon3-7B-Instruct-1.58bit,tiiuae/Falcon3-10B-Instruct-1.58bit}
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Model used for inference
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--model-dir MODEL_DIR, -md MODEL_DIR
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Directory to save/load the model
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--log-dir LOG_DIR, -ld LOG_DIR
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Directory to save the logging info
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--quant-type {i2_s,tl1}, -q {i2_s,tl1}
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Quantization type
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--quant-embd Quantize the embeddings to f16
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--use-pretuned, -p Use the pretuned kernel parameters
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</pre>
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## Usage
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### Basic usage
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```bash
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# Run inference with the quantized model
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python run_inference.py -m models/BitNet-b1.58-2B-4T/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
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```
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<pre>
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usage: run_inference.py [-h] [-m MODEL] [-n N_PREDICT] -p PROMPT [-t THREADS] [-c CTX_SIZE] [-temp TEMPERATURE] [-cnv]
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Run inference
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optional arguments:
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-h, --help show this help message and exit
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-m MODEL, --model MODEL
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Path to model file
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-n N_PREDICT, --n-predict N_PREDICT
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Number of tokens to predict when generating text
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-p PROMPT, --prompt PROMPT
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Prompt to generate text from
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-t THREADS, --threads THREADS
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Number of threads to use
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-c CTX_SIZE, --ctx-size CTX_SIZE
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Size of the prompt context
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-temp TEMPERATURE, --temperature TEMPERATURE
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Temperature, a hyperparameter that controls the randomness of the generated text
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-cnv, --conversation Whether to enable chat mode or not (for instruct models.)
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(When this option is turned on, the prompt specified by -p will be used as the system prompt.)
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</pre>
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### Demo
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A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
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https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
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### Benchmark
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We provide scripts to run the inference benchmark providing a model.
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```
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usage: e2e_benchmark.py -m MODEL [-n N_TOKEN] [-p N_PROMPT] [-t THREADS]
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Setup the environment for running the inference
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required arguments:
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-m MODEL, --model MODEL
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Path to the model file.
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optional arguments:
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-h, --help
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Show this help message and exit.
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-n N_TOKEN, --n-token N_TOKEN
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Number of generated tokens.
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-p N_PROMPT, --n-prompt N_PROMPT
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Prompt to generate text from.
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-t THREADS, --threads THREADS
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Number of threads to use.
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```
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Here's a brief explanation of each argument:
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- `-m`, `--model`: The path to the model file. This is a required argument that must be provided when running the script.
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- `-n`, `--n-token`: The number of tokens to generate during the inference. It is an optional argument with a default value of 128.
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- `-p`, `--n-prompt`: The number of prompt tokens to use for generating text. This is an optional argument with a default value of 512.
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- `-t`, `--threads`: The number of threads to use for running the inference. It is an optional argument with a default value of 2.
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- `-h`, `--help`: Show the help message and exit. Use this argument to display usage information.
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|
|
|
For example:
|
|
|
|
```sh
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python utils/e2e_benchmark.py -m /path/to/model -n 200 -p 256 -t 4
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|
```
|
|
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|
This command would run the inference benchmark using the model located at `/path/to/model`, generating 200 tokens from a 256 token prompt, utilizing 4 threads.
|
|
|
|
For the model layout that do not supported by any public model, we provide scripts to generate a dummy model with the given model layout, and run the benchmark on your machine:
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|
|
|
```bash
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|
python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile models/dummy-bitnet-125m.tl1.gguf --outtype tl1 --model-size 125M
|
|
|
|
# Run benchmark with the generated model, use -m to specify the model path, -p to specify the prompt processed, -n to specify the number of token to generate
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|
python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128
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|
```
|
|
|
|
### Convert from `.safetensors` Checkpoints
|
|
|
|
```sh
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|
# Prepare the .safetensors model file
|
|
huggingface-cli download microsoft/bitnet-b1.58-2B-4T-bf16 --local-dir ./models/bitnet-b1.58-2B-4T-bf16
|
|
|
|
# Convert to gguf model
|
|
python ./utils/convert-helper-bitnet.py ./models/bitnet-b1.58-2B-4T-bf16
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|
```
|
|
|
|
## Acknowledgements
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|
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|
This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp) framework. We would like to thank all the authors for their contributions to the open-source community. Also, bitnet.cpp's kernels are built on top of the Lookup Table methodologies pioneered in [T-MAC](https://github.com/microsoft/T-MAC/). For inference of general low-bit LLMs beyond ternary models, we recommend using T-MAC.
|
|
|
|
### FAQ (Frequently Asked Questions)📌
|
|
|
|
#### Q1: The build dies with errors building llama.cpp due to issues with std::chrono in log.cpp?
|
|
|
|
**A:**
|
|
This is an issue introduced in recent version of llama.cpp. Please refer to this [commit](https://github.com/tinglou/llama.cpp/commit/4e3db1e3d78cc1bcd22bcb3af54bd2a4628dd323) in the [discussion](https://github.com/abetlen/llama-cpp-python/issues/1942) to fix this issue.
|
|
|
|
#### Q2: How to build with clang in conda environment on windows?
|
|
|
|
**A:**
|
|
Before building the project, verify your clang installation and access to Visual Studio tools by running:
|
|
```
|
|
clang -v
|
|
```
|
|
|
|
This command checks that you are using the correct version of clang and that the Visual Studio tools are available. If you see an error message such as:
|
|
```
|
|
'clang' is not recognized as an internal or external command, operable program or batch file.
|
|
```
|
|
|
|
It indicates that your command line window is not properly initialized for Visual Studio tools.
|
|
|
|
• If you are using Command Prompt, run:
|
|
```
|
|
"C:\Program Files\Microsoft Visual Studio\2022\Professional\Common7\Tools\VsDevCmd.bat" -startdir=none -arch=x64 -host_arch=x64
|
|
```
|
|
|
|
• If you are using Windows PowerShell, run the following commands:
|
|
```
|
|
Import-Module "C:\Program Files\Microsoft Visual Studio\2022\Professional\Common7\Tools\Microsoft.VisualStudio.DevShell.dll" Enter-VsDevShell 3f0e31ad -SkipAutomaticLocation -DevCmdArguments "-arch=x64 -host_arch=x64"
|
|
```
|
|
|
|
These steps will initialize your environment and allow you to use the correct Visual Studio tools.
|