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Revamp README with structured layout, News section, and Model Releases (#590)

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<div align="center">
# bitnet.cpp
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
![version](https://img.shields.io/badge/version-1.0-blue)
[![Hugging Face](https://img.shields.io/badge/HuggingFace-Collection-orange?logo=huggingface)](https://huggingface.co/collections/microsoft/bitnet)
[![Technical Report](https://img.shields.io/badge/Technical-Report-red?logo=arxiv)](https://arxiv.org/abs/2502.11880)
[![Demo](https://img.shields.io/badge/Online-Demo-green?logo=microsoft)](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/)
[![GPU Kernel](https://img.shields.io/badge/GPU-Kernel-6F42C1?logo=github)](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)
[<img src="./assets/header_embedding_release.png" alt="BitNet Embeddings on Hugging Face" width="800"/>](https://huggingface.co/microsoft/BitNet-embedding-0.6B)
</div>
We are excited to release **[BitNet-embedding-0.6B](https://huggingface.co/microsoft/BitNet-embedding-0.6B)** and **[BitNet-embedding-270M](https://huggingface.co/microsoft/BitNet-embedding-270M)**, the first 1-bit embedding models that deliver competitive embedding quality with significantly faster inference on CPUs. Key highlights:
<div align="left">
- **1.42x to 2.28x speedup** over F16 on bitnet-embedding-0.6B prefill (8 threads)
- **1.32x to 1.74x speedup** over F16 on bitnet-embedding-270M prefill (8 threads)
<h3>📰 News</h3>
<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. ![NEW](https://img.shields.io/badge/NEW-red)
- **1.42x to 2.28x speedup** over F16 on BitNet-embedding-0.6B prefill (8 threads)
- **1.32x to 1.74x speedup** over F16 on BitNet-embedding-270M prefill (8 threads)
- Supports I2_S conversion with optimized kernels on x86 CPUs
- Lossless inference with 2 bits per weight
<img src="./assets/embedding_prefill_performance.png" alt="BitNet Embedding Prefill Performance" width="800"/>
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.
For detailed usage and technical information, see the [BitNet Embeddings I2_S Guide](docs/bitnet-embeddings-i2s-guide.md).
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.
## About bitnet.cpp
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.
Try it out via this [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).
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.
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 will coming next).
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.
The first release of bitnet.cpp is to support inference on CPUs. 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.
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.
**Latest optimization** introduces parallel kernel implementations with configurable tiling and embedding quantization support, achieving **1.15x to 2.1x** additional speedup over the original implementation across different hardware platforms and workloads. For detailed technical information, see the [optimization guide](src/README.md).
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.
10/17/2024: 📣 bitnet.cpp 1.0 released.
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)
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.
10/17/2023: 📑 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453) — the original BitNet paper.
</div>
## Overview
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).
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).
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.
<img src="./assets/performance.png" alt="performance_comparison" width="800"/>
## Demo
## Model Releases
A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
### 1. [BitNet-b1.58-2B-4T](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T) - 1-bit Large Language Model
https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
**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.
## What's New:
- 07/20/2026 [BitNet-embedding-0.6B and BitNet-embedding-270M on Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-0.6B) ![NEW](https://img.shields.io/badge/NEW-red)
- 07/16/2026 [BitNet Embeddings 0.6B/270M: I2_S Conversion and Inference Optimization](docs/bitnet-embeddings-i2s-guide.md)
- 01/15/2026 [BitNet CPU Inference Optimization](https://github.com/microsoft/BitNet/blob/main/src/README.md)
- 05/20/2025 [BitNet Official GPU inference kernel](https://github.com/microsoft/BitNet/blob/main/gpu/README.md)
- 04/14/2025 [BitNet Official 2B Parameter Model on Hugging Face](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)
- 02/18/2025 [Bitnet.cpp: Efficient Edge Inference for Ternary LLMs](https://arxiv.org/abs/2502.11880)
- 11/08/2024 [BitNet a4.8: 4-bit Activations for 1-bit LLMs](https://arxiv.org/abs/2411.04965)
- 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)
- 10/17/2024 bitnet.cpp 1.0 released.
- 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)
- 02/27/2024 [The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits](https://arxiv.org/abs/2402.17764)
- 10/17/2023 [BitNet: Scaling 1-bit Transformers for Large Language Models](https://arxiv.org/abs/2310.11453)
- **Fast CPU Inference**: Achieves up to **6.17x speedup** on x86 CPUs and **5.07x** on ARM CPUs compared to full-precision models.
- **Energy Efficient**: Reduces energy consumption by up to **82.2%** on x86 and **70.0%** on ARM.
- **GPU Support**: Official GPU inference kernel available for accelerated deployment.
- **Chat-Ready**: Supports conversational mode for interactive use.
## Acknowledgements
[🤗 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)
<img src="./assets/bitnet_b1.58_2b_benchmark.png" alt="BitNet b1.58 2B Benchmark" width="600"/>
### 2. [BitNet-embedding-0.6B](https://huggingface.co/microsoft/BitNet-embedding-0.6B) - 1-bit Embedding Model
**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.
- **1.42x to 2.28x speedup** over F16 on prefill (8 threads, x86)
- **Lossless Quality**: Competitive embedding quality with 2 bits per weight
- **I2_S Kernel**: Supports optimized I2_S conversion on x86 CPUs
[🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-0.6B) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md)
<img src="./assets/embedding_prefill_0.6B.png" alt="BitNet Embedding 0.6B Prefill Performance" width="600"/>
### 3. [BitNet-embedding-270M](https://huggingface.co/microsoft/BitNet-embedding-270M) - Lightweight 1-bit Embedding Model
**BitNet-embedding-270M** is a compact **270M-parameter** 1-bit embedding model designed for resource-constrained environments, offering fast inference with minimal memory footprint.
- **1.32x to 1.74x speedup** over F16 on prefill (8 threads, x86)
- **Lossless Quality**: Competitive embedding quality with 2 bits per weight
- **Lightweight**: Only 270M parameters for edge deployment scenarios
[🤗 Hugging Face](https://huggingface.co/microsoft/BitNet-embedding-270M) | [📄 I2_S Guide](docs/bitnet-embeddings-i2s-guide.md)
<img src="./assets/embedding_prefill_270M.png" alt="BitNet Embedding 270M Prefill Performance" width="600"/>
## Supported Models
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.
## Official Models
<table>
</tr>
<tr>
<th rowspan="2">Model</th>
<th rowspan="2">Parameters</th>
@@ -64,6 +109,9 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)
<th>TL1</th>
<th>TL2</th>
</tr>
<tr>
<th colspan="6" style="text-align:left;">Official Models</th>
</tr>
<tr>
<td rowspan="2"><a href="https://huggingface.co/microsoft/BitNet-b1.58-2B-4T">BitNet-b1.58-2B-4T</a></td>
<td rowspan="2">2.4B</td>
@@ -106,23 +154,8 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)
<td>&#10060;</td>
<td>&#10060;</td>
</tr>
</table>
## Supported Models
❗️**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.**
<table>
</tr>
<tr>
<th rowspan="2">Model</th>
<th rowspan="2">Parameters</th>
<th rowspan="2">CPU</th>
<th colspan="3">Kernel</th>
</tr>
<tr>
<th>I2_S</th>
<th>TL1</th>
<th>TL2</th>
<th colspan="6" style="text-align:left;">Community Models</th>
</tr>
<tr>
<td rowspan="2"><a href="https://huggingface.co/1bitLLM/bitnet_b1_58-large">bitnet_b1_58-large</a></td>
@@ -196,7 +229,7 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)
</tr>
</table>
❗️**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.**
## Installation
@@ -259,6 +292,7 @@ optional arguments:
--quant-embd Quantize the embeddings to f16
--use-pretuned, -p Use the pretuned kernel parameters
</pre>
## Usage
### Basic usage
```bash
@@ -288,6 +322,12 @@ optional arguments:
(When this option is turned on, the prompt specified by -p will be used as the system prompt.)
</pre>
### Demo
A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
### Benchmark
We provide scripts to run the inference benchmark providing a model.
@@ -346,6 +386,10 @@ huggingface-cli download microsoft/bitnet-b1.58-2B-4T-bf16 --local-dir ./models/
python ./utils/convert-helper-bitnet.py ./models/bitnet-b1.58-2B-4T-bf16
```
## Acknowledgements
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?
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