libllm

Efficient inference of large language models.

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libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

README:

libLLM: Efficient inference of large language models.

Welcome to libLLM, an open-source project designed for efficient inference of large language models (LLM) on ordinary personal computers and mobile devices. The core is implemented in C++14, without any third-party dependencies (such as BLAS or SentencePiece), enabling seamless operation across a variety of devices.

欢迎使用libLLM，这是一个专为在普通个人电脑和移动设备上高效推理大型语言模型（LLM）而设计的开源项目。核心使用C++14编写，没有第三方依赖（BLAS、SentencePiece等），能在各种设备中无缝运行。

Model download:

Model	Download	llm Command
Index-1.9B-Character (Role-playing)	[🤗HF] [MS]	llm chat -m index:character
Index-1.9B-Chat	[🤗HF] [MS]	llm chat -m index
Qwen2-1.5B-Instruct	[🤗HF] [MS]	llm chat -m qwen:1.5b
Qwen2-7B-Instruct	[🤗HF] [MS]	llm chat -m qwen:7b
Whisper-large-v3	[🤗HF] [MS]	llm transcribe -m whisper

HF = HuggingFace, MS = ModelScope

Recent updates

[2024-08-12] Support whisper models.
[2024-08-02] Support the translation command in llm.
[2024-07-30] Support model downloading from huggingface. For example, llm chat -model index-character will automatically download the index-character model from 🤗Huggingface.

Quickstart

To run and chat with Bilibili-Index-1.9B-Character:

$ llm chat -m index-character

Key features:

Optimized for everyday devices: libLLM has been optimized to run smoothly on common personal computers, ensuring the powerful capabilities of large language models are accessible to a wider range of users.
C++ code: Written in standard C++14, it is simple and efficient.
No external dependencies: The core functionality does not require third-party dependencies (BLAS, SentencePiece, etc.), and the necessary GEMM kernels are implemented internally (avx2, avx512).
CUDA support: Supports accelerated inference using CUDA.

特点

为日常设备进行优化：libLLM经过优化，可在常见的个人电脑上平稳运行，确保大型语言模型的强大功能面向更广泛的用户。
C++代码：采用标准C++14编写，简单高效。
无外部依赖：核心功能无需第三方依赖（BLAS、SentencePiece等），所需的GEMM内核均在内部实现(avx2、avx512)。
支持CUDA：支持使用CUDA加速推理。

Build

libLLM CPU only

$ mkdir build && cd build
$ cmake ..
$ make -j

For macOS

Please brew install OpenMP before cmake. NOTE: currently libllm macOS expected to be very slow since there is no aarch64 kernel for it.

% brew install libomp
% export OpenMP_ROOT=$(brew --prefix)/opt/libomp
% mkdir build && cd build
% cmake ..
% make -j

Build with CUDA

NOTE: specify -DCUDAToolkit_ROOT=<CUDA-DIR> if there is multiple CUDA versions in your OS.

Recommand versions are:

CUDA: 11.7

$ mkdir build && cd build
$ cmake -DWITH_CUDA=ON [-DCUDAToolkit_ROOT=<CUDA-DIR>] ..
$ make -j

Run libllm command line

$ src/libllm/llm chat -m index-character
INFO 2024-07-30T12:02:28Z interface.cc:67] ISA support: AVX2=1 F16C=1 AVX512F=1
INFO 2024-07-30T12:02:28Z interface.cc:71] Use Avx512 backend.
INFO 2024-07-30T12:02:30Z matmul.cc:43] Use GEMM from cuBLAS.
INFO 2024-07-30T12:02:30Z cuda_operators.cc:51] cuda numDevices = 2
INFO 2024-07-30T12:02:30Z cuda_operators.cc:52] cuda:0 maxThreadsPerMultiProcessor = 2048
INFO 2024-07-30T12:02:30Z cuda_operators.cc:54] cuda:0 multiProcessorCount = 20
INFO 2024-07-30T12:02:30Z thread_pool.cc:73] ThreadPool started. numThreads=20
INFO 2024-07-30T12:02:30Z llm.cc:204] read model package: /home/xiaoych/.libllm/models/bilibili-index-1.9b-character-q4.llmpkg
INFO 2024-07-30T12:02:30Z model_for_generation.cc:43] model_type = index
INFO 2024-07-30T12:02:30Z model_for_generation.cc:44] device = cuda
INFO 2024-07-30T12:02:31Z state_map.cc:66] 220 tensors read.
Please input your question.
    Type ':new' to start a new session (clean history).
    Type ':sys <system_prompt>' to set the system prompt and start a new session .
> hi
您好！我是Index，请问有什么我可以帮助您的吗？
(12 tokens, time=0.76s, 63.47ms per token)
>

API Examples

Python

from libllm import Model, ControlToken

model = Model("tools/bilibili_index.llmpkg")
prompt = [ControlToken("<|reserved_0|>"), "hi", ControlToken("<|reserved_1|>")]

for chunk in model.complete(prompt):
    print(chunk.text, end="", flush=True)

print("\nDone!")

Go

package main

import (
    "fmt"
    "log"

    "github.com/ling0322/libllm/go/llm"
)

func main() {
    model, err := llm.NewModel("../../tools/bilibili_index.llmpkg", llm.Auto)
    if err != nil {
        log.Fatal(err)
    }

    prompt := llm.NewPrompt()
    prompt.AppendControlToken("<|reserved_0|>")
    prompt.AppendText("hi")
    prompt.AppendControlToken("<|reserved_1|>")
    comp, err := model.Complete(llm.NewCompletionConfig(), prompt)
    if err != nil {
        log.Fatal(err)
    }

    for comp.IsActive() {
        chunk, err := comp.GenerateNextChunk()
        if err != nil {
            log.Fatal(err)
        }

        fmt.Print(chunk.Text)
    }
    fmt.Println()
}

Export Huggingface models

Here is an example of exporting Index-1.9B model from huggingface.

$ cd tools
$ python bilibili_index_exporter.py \
    -huggingface_name IndexTeam/Index-1.9B-Character \
    -quant q4  \
    -output index.llmpkg

Then all required modules realted to IndexTeam/Index-1.9B-Character, including model, tokenizer and configs will be written to index.llmpkg.

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