docs: make Chinese README the default

2026-07-13 10:25:34 +00:00
parent e2e926c3b4
commit f19a5b421e
1 changed files with 77 additions and 70 deletions
@@ -1,27 +1,33 @@
+<!-- WEHUB_ZH_README -->
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
+> [English](./README.en.md) · [原始项目](https://github.com/microsoft/BitNet) · [上游 README](https://github.com/microsoft/BitNet/blob/HEAD/README.md)  
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
 # 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)

-[<img src="./assets/header_model_release.png" alt="BitNet Model on Hugging Face" width="800"/>](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)
+[<img src="./assets/header_model_release.png" alt="Hugging Face 上的 BitNet 模型" width="800"/>](https://huggingface.co/microsoft/BitNet-b1.58-2B-4T)

-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).
+通过此 [演示](https://demo-bitnet-h0h8hcfqeqhrf5gf.canadacentral-01.azurewebsites.net/), 试用，或在你自己的 [CPU](https://github.com/microsoft/BitNet?tab=readme-ov-file#build-from-source) 或 [GPU](https://github.com/microsoft/BitNet/blob/main/gpu/README.md). 上构建并运行。

-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).
+bitnet.cpp 是 1-bit LLM（Large Language Model，大语言模型；例如 BitNet b1.58）的官方推理框架。它提供一系列优化的 kernel，在 CPU 和 GPU 上支持 1.58-bit 模型的**快速**且**无损**推理（NPU 支持即将推出）。

-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.
+bitnet.cpp 的首个版本支持在 CPU 上进行推理。在 ARM CPU 上，bitnet.cpp 可实现 **1.37x** 至 **5.07x** 的加速，模型越大，性能提升越明显。此外，能耗降低 **55.4%** 至 **70.0%**，进一步提升整体效率。在 x86 CPU 上，加速范围为 **2.37x** 至 **6.17x**，能耗降低 **71.9%** 至 **82.2%**。此外，bitnet.cpp 可在单颗 CPU 上运行 100B BitNet b1.58 模型，速度可达人类阅读水平（每秒 5-7 个 token），显著提升在本地设备上运行 LLM 的潜力。更多细节请参阅[技术报告](https://arxiv.org/abs/2410.16144)。

-**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).
+**最新优化**引入了支持可配置 tiling 的并行 kernel 实现，以及 embedding 量化支持，在不同硬件平台和工作负载上，相比原始实现可获得 **1.15x 至 2.1x** 的额外加速。详细技术信息请参阅[优化指南](src/README.md)。

 <img src="./assets/performance.png" alt="performance_comparison" width="800"/>


-## Demo
+## 演示

-A demo of bitnet.cpp running a BitNet b1.58 3B model on Apple M2:
+在 Apple M2 上运行 BitNet b1.58 3B 模型的 bitnet.cpp 演示：

 https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1

-## What's New:
+## 最新动态：
 - 01/15/2026 [BitNet CPU Inference Optimization](https://github.com/microsoft/BitNet/blob/main/src/README.md) ![NEW](https://img.shields.io/badge/NEW-red)
 - 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)
@@ -33,15 +39,15 @@ https://github.com/user-attachments/assets/7f46b736-edec-4828-b809-4be780a3e5b1
 - 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)

-## Acknowledgements
+## 致谢

-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
+本项目基于 [llama.cpp](https://github.com/ggerganov/llama.cpp) 框架。我们感谢所有作者对开源社区的贡献。此外，bitnet.cpp 的 kernel 构建于 [T-MAC](https://github.com/microsoft/T-MAC/). 开创的查找表（Lookup Table）方法之上。对于三值模型之外的一般低位 LLM 推理，我们建议使用 T-MAC。
+## 官方模型
 <table>
    </tr>
    <tr>
-        <th rowspan="2">Model</th>
-        <th rowspan="2">Parameters</th>
+        <th rowspan="2">模型</th>
+        <th rowspan="2">参数量</th>
        <th rowspan="2">CPU</th>
        <th colspan="3">Kernel</th>
    </tr>
@@ -66,14 +72,14 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)
    </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.**
+## 支持的模型
+❗️**我们使用 [Hugging Face](https://huggingface.co/) 上现有的 1-bit LLM 来展示 bitnet.cpp 的推理能力。我们希望 bitnet.cpp 的发布能够激励 1-bit LLM 在模型规模和训练 token 数量等方面的大规模发展。**

 <table>
    </tr>
    <tr>
-        <th rowspan="2">Model</th>
-        <th rowspan="2">Parameters</th>
+        <th rowspan="2">模型</th>
+        <th rowspan="2">参数量</th>
        <th rowspan="2">CPU</th>
        <th colspan="3">Kernel</th>
    </tr>
@@ -156,34 +162,34 @@ This project is based on the [llama.cpp](https://github.com/ggerganov/llama.cpp)



-## Installation
+## 安装

-### Requirements
+### 环境要求
 - python>=3.9
 - cmake>=3.22
 - clang>=18
-    - 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):
+    - 对于 Windows 用户，请安装 [Visual Studio 2022](https://visualstudio.microsoft.com/downloads/).。在安装程序中，请至少勾选以下选项（这也会自动安装 CMake 等所需的其他工具）：
        -  Desktop-development with C++
        -  C++-CMake Tools for Windows
        -  Git for Windows
        -  C++-Clang Compiler for Windows
        -  MS-Build Support for LLVM-Toolset (clang)
-    - For Debian/Ubuntu users, you can download with [Automatic installation script](https://apt.llvm.org/)
+    - 对于 Debian/Ubuntu 用户，可通过[自动安装脚本](https://apt.llvm.org/) 下载：

        `bash -c "$(wget -O - https://apt.llvm.org/llvm.sh)"`
- conda (highly recommend)
+- conda（强烈推荐）

-### Build from source
+### 从源码构建

 > [!IMPORTANT]
-> 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.
+> 如果你使用的是 Windows，请务必始终使用 VS2022 的 Developer Command Prompt / PowerShell 执行以下命令。如遇到问题，请参阅下方的常见问题解答（FAQs）。

-1. Clone the repo
+1. 克隆仓库
 ```bash
 git clone --recursive https://github.com/microsoft/BitNet.git
 cd BitNet
 ```
-2. Install the dependencies
+2. 安装依赖
 ```bash
 # (Recommended) Create a new conda environment
 conda create -n bitnet-cpp python=3.9
@@ -191,7 +197,7 @@ conda activate bitnet-cpp

 pip install -r requirements.txt
 ```
-3. Build the project
+3. 构建项目
 ```bash
 # Manually download the model and run with local path
 huggingface-cli download microsoft/BitNet-b1.58-2B-4T-gguf --local-dir models/BitNet-b1.58-2B-4T
@@ -201,24 +207,25 @@ python setup_env.py -md models/BitNet-b1.58-2B-4T -q i2_s
 <pre>
 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]
                    [--use-pretuned]
+</pre>

-Setup the environment for running inference
+配置用于运行推理的环境

 optional arguments:
-  -h, --help            show this help message and exit
+  -h, --help            显示此帮助信息并退出
  --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}
-                        Model used for inference
+                        用于推理的模型
  --model-dir MODEL_DIR, -md MODEL_DIR
-                        Directory to save/load the model
+                        保存/加载模型的目录
  --log-dir LOG_DIR, -ld LOG_DIR
-                        Directory to save the logging info
+                        保存日志信息的目录
  --quant-type {i2_s,tl1}, -q {i2_s,tl1}
-                        Quantization type
-  --quant-embd          Quantize the embeddings to f16
-  --use-pretuned, -p    Use the pretuned kernel parameters
+                        量化类型
+  --quant-embd          将嵌入层量化到 f16
+  --use-pretuned, -p    使用预调优的内核参数
 </pre>
-## Usage
-### Basic usage
+## 用法
+### 基本用法
 ```bash
 # Run inference with the quantized model
 python run_inference.py -m models/BitNet-b1.58-2B-4T/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
@@ -229,25 +236,25 @@ usage: run_inference.py [-h] [-m MODEL] [-n N_PREDICT] -p PROMPT [-t THREADS] [-
 Run inference

 optional arguments:
-  -h, --help            show this help message and exit
+  -h, --help            显示此帮助信息并退出
  -m MODEL, --model MODEL
-                        Path to model file
+                        模型文件路径
  -n N_PREDICT, --n-predict N_PREDICT
-                        Number of tokens to predict when generating text
+                        生成文本时要预测的 token 数量
  -p PROMPT, --prompt PROMPT
-                        Prompt to generate text from
+                        用于生成文本的提示词
  -t THREADS, --threads THREADS
-                        Number of threads to use
+                        使用的线程数
  -c CTX_SIZE, --ctx-size CTX_SIZE
-                        Size of the prompt context
+                        提示词上下文大小
  -temp TEMPERATURE, --temperature TEMPERATURE
-                        Temperature, a hyperparameter that controls the randomness of the generated text
-  -cnv, --conversation  Whether to enable chat mode or not (for instruct models.)
-                        (When this option is turned on, the prompt specified by -p will be used as the system prompt.)
+                        Temperature（温度），控制生成文本随机性的超参数
+  -cnv, --conversation  是否启用聊天模式（适用于 instruct 模型。）
+                        （启用此选项时，-p 指定的提示词将作为系统提示词使用。）
 </pre>

-### Benchmark
-We provide scripts to run the inference benchmark providing a model.
+### 基准测试
+我们提供了脚本，可在给定模型的情况下运行推理基准测试。

 ```  
 usage: e2e_benchmark.py -m MODEL [-n N_TOKEN] [-p N_PROMPT] [-t THREADS]  
@@ -269,23 +276,23 @@ optional arguments:
                        Number of threads to use. 
 ```  
   
-Here's a brief explanation of each argument:  
+以下简要说明各参数：  
   
- `-m`, `--model`: The path to the model file. This is a required argument that must be provided when running the script.  
- `-n`, `--n-token`: The number of tokens to generate during the inference. It is an optional argument with a default value of 128.  
- `-p`, `--n-prompt`: The number of prompt tokens to use for generating text. This is an optional argument with a default value of 512.  
- `-t`, `--threads`: The number of threads to use for running the inference. It is an optional argument with a default value of 2.  
- `-h`, `--help`: Show the help message and exit. Use this argument to display usage information.  
+- `-m`、`--model`：模型文件路径。运行脚本时必须提供此必填参数。  
+- `-n`、`--n-token`：推理过程中要生成的 token 数量。可选参数，默认值为 128。  
+- `-p`、`--n-prompt`：用于生成文本的提示词 token 数量。可选参数，默认值为 512。  
+- `-t`、`--threads`：运行推理时使用的线程数。可选参数，默认值为 2。  
+- `-h`、`--help`：显示帮助信息并退出。使用此参数可查看用法说明。  
   
-For example:  
+例如：  
   
 ```sh  
 python utils/e2e_benchmark.py -m /path/to/model -n 200 -p 256 -t 4  
 ```  
   
-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.  
+此命令将使用位于 `/path/to/model` 的模型运行推理基准测试，从 256 个 token 的提示词生成 200 个 token，并使用 4 个线程。  

-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:
+对于没有任何公开模型支持的模型布局，我们提供了脚本，可根据给定模型布局生成虚拟模型，并在你的机器上运行基准测试：

 ```bash
 python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile models/dummy-bitnet-125m.tl1.gguf --outtype tl1 --model-size 125M
@@ -294,7 +301,7 @@ python utils/generate-dummy-bitnet-model.py models/bitnet_b1_58-large --outfile
 python utils/e2e_benchmark.py -m models/dummy-bitnet-125m.tl1.gguf -p 512 -n 128
 ```

-### Convert from `.safetensors` Checkpoints
+### 从 `.safetensors` 检查点转换

 ```sh
 # Prepare the .safetensors model file
@@ -304,36 +311,36 @@ 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
 ```

-### FAQ (Frequently Asked Questions)📌 
+### 常见问题（FAQ）📌 

-#### Q1: The build dies with errors building llama.cpp due to issues with std::chrono in log.cpp?
+#### Q1：构建时因在 log.cpp 中 std::chrono 相关问题导致编译 llama.cpp 失败？

-**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.
+**A：**
+这是 llama.cpp 近期版本中引入的问题。请参阅此 [commit](https://github.com/tinglou/llama.cpp/commit/4e3db1e3d78cc1bcd22bcb3af54bd2a4628dd323) in the [discussion](https://github.com/abetlen/llama-cpp-python/issues/1942) 以修复此问题。

-#### Q2: How to build with clang in conda environment on windows?
+#### Q2：如何在 Windows 上的 conda 环境中使用 clang 构建？

-**A:** 
-Before building the project, verify your clang installation and access to Visual Studio tools by running:
+**A：** 
+在构建项目之前，请运行以下命令验证 clang 安装及 Visual Studio 工具是否可用：
 ```
 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:
+此命令会检查你是否使用了正确版本的 clang，以及 Visual Studio 工具是否可用。如果你看到如下错误信息：
 ```
 '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.
+这表明你的命令行窗口未正确初始化 Visual Studio 工具。

-• If you are using Command Prompt, run:
+• 如果你使用命令提示符（Command Prompt），请运行：
 ```
 "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:
+• 如果你使用 Windows PowerShell，请运行以下命令：
 ```
 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.
+这些步骤将初始化你的环境，使你能够使用正确的 Visual Studio 工具。