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Llama.cpp

llama-cpp-python 是 llama.cpp 的 Python 绑定。

它支持对 许多 LLMs 模型的推理，这些模型可以在 Hugging Face 上访问。

本笔记本介绍了如何在 LangChain 中运行 llama-cpp-python。

注意：llama-cpp-python 的新版本使用 GGUF 模型文件（见 这里）。

这是一个重大变更。

要将现有的 GGML 模型转换为 GGUF，您可以在 llama.cpp 中运行以下命令：

python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input models/openorca-platypus2-13b.ggmlv3.q4_0.bin --output models/openorca-platypus2-13b.gguf.q4_0.bin

安装​

有多种安装 llama-cpp 包的选项：

  • CPU 使用
  • CPU + GPU（使用众多 BLAS 后端之一）
  • Metal GPU（适用于搭载 Apple Silicon 芯片的 MacOS）

仅 CPU 安装​

%pip install --upgrade --quiet  llama-cpp-python

使用 OpenBLAS / cuBLAS / CLBlast 安装​

llama.cpp 支持多个 BLAS 后端以实现更快的处理速度。使用 FORCE_CMAKE=1 环境变量强制使用 cmake 并安装所需 BLAS 后端的 pip 包 (source)。

使用 cuBLAS 后端的示例安装：

!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python

重要：如果您已经安装了仅支持 CPU 的版本，您需要从头重新安装。请考虑以下命令： 

!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir

使用 Metal 安装​

llama.cpp 支持 Apple silicon 的第一公民 - 通过 ARM NEON、Accelerate 和 Metal 框架进行了优化。使用 FORCE_CMAKE=1 环境变量强制使用 cmake 并安装支持 Metal 的 pip 包 (source)。

使用 Metal 支持的示例安装：

!CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python

重要：如果您已经安装了仅支持 CPU 的版本，您需要从头开始重新安装：考虑以下命令： 

!CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir

在Windows上安装​

通过从源代码编译来安装llama-cpp-python库是稳定的。您可以遵循仓库中的大部分说明，但有一些特定于Windows的说明可能会很有用。

安装llama-cpp-python的要求：

  • git
  • python
  • cmake
  • Visual Studio Community（确保您使用以下设置进行安装）
    • C++桌面开发
    • Python开发
    • C++嵌入式Linux开发
  1. 递归克隆git仓库以获取llama.cpp子模块
git clone --recursive -j8 https://github.com/abetlen/llama-cpp-python.git
  1. 打开命令提示符并设置以下环境变量。
set FORCE_CMAKE=1
set CMAKE_ARGS=-DLLAMA_CUBLAS=OFF

如果您有NVIDIA GPU，请确保将DLLAMA_CUBLAS设置为ON

编译和安装​

现在您可以cd进入llama-cpp-python目录并安装该包

python -m pip install -e .

重要：如果您已经安装了仅限CPU的版本，您需要从头重新安装：考虑以下命令：

!python -m pip install -e . --force-reinstall --no-cache-dir

使用方法​

确保您遵循所有说明以安装所有必要的模型文件。

您不需要 API_TOKEN，因为您将本地运行 LLM。

了解哪些模型适合在所需机器上使用是值得的。

TheBloke's Hugging Face 模型有一个 Provided files 部分，展示了运行不同量化大小和方法的模型所需的 RAM（例如：Llama2-7B-Chat-GGUF）。

这个 github issue 也与找到适合您机器的正确模型相关。

from langchain_community.llms import LlamaCpp
from langchain_core.callbacks import CallbackManager, StreamingStdOutCallbackHandler
from langchain_core.prompts import PromptTemplate

考虑使用适合您模型的模板！请查看 Hugging Face 等的模型页面以获取正确的提示模板。

template = """Question: {question}

Answer: Let's work this out in a step by step way to be sure we have the right answer."""

prompt = PromptTemplate.from_template(template)
# Callbacks support token-wise streaming
callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])

CPU​

使用 LLaMA 2 7B 模型的示例

# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
    temperature=0.75,
    max_tokens=2000,
    top_p=1,
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)
question = """
Question: A rap battle between Stephen Colbert and John Oliver
"""
llm.invoke(question)

Stephen Colbert:
Yo, John, I heard you've been talkin' smack about me on your show.
Let me tell you somethin', pal, I'm the king of late-night TV
My satire is sharp as a razor, it cuts deeper than a knife
While you're just a british bloke tryin' to be funny with your accent and your wit.
John Oliver:
Oh Stephen, don't be ridiculous, you may have the ratings but I got the real talk.
My show is the one that people actually watch and listen to, not just for the laughs but for the facts.
While you're busy talkin' trash, I'm out here bringing the truth to light.
Stephen Colbert:
Truth? Ha! You think your show is about truth? Please, it's all just a joke to you.
You're just a fancy-pants british guy tryin' to be funny with your news and your jokes.
While I'm the one who's really makin' a difference, with my sat
``````output

llama_print_timings:        load time =   358.60 ms
llama_print_timings:      sample time =   172.55 ms /   256 runs   (    0.67 ms per token,  1483.59 tokens per second)
llama_print_timings: prompt eval time =   613.36 ms /    16 tokens (   38.33 ms per token,    26.09 tokens per second)
llama_print_timings:        eval time = 10151.17 ms /   255 runs   (   39.81 ms per token,    25.12 tokens per second)
llama_print_timings:       total time = 11332.41 ms
"\nStephen Colbert:\nYo, John, I heard you've been talkin' smack about me on your show.\nLet me tell you somethin', pal, I'm the king of late-night TV\nMy satire is sharp as a razor, it cuts deeper than a knife\nWhile you're just a british bloke tryin' to be funny with your accent and your wit.\nJohn Oliver:\nOh Stephen, don't be ridiculous, you may have the ratings but I got the real talk.\nMy show is the one that people actually watch and listen to, not just for the laughs but for the facts.\nWhile you're busy talkin' trash, I'm out here bringing the truth to light.\nStephen Colbert:\nTruth? Ha! You think your show is about truth? Please, it's all just a joke to you.\nYou're just a fancy-pants british guy tryin' to be funny with your news and your jokes.\nWhile I'm the one who's really makin' a difference, with my sat"

使用 LLaMA v1 模型的示例

# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="./ggml-model-q4_0.bin", callback_manager=callback_manager, verbose=True
)
llm_chain = prompt | llm
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.invoke({"question": question})


1. First, find out when Justin Bieber was born.
2. We know that Justin Bieber was born on March 1, 1994.
3. Next, we need to look up when the Super Bowl was played in that year.
4. The Super Bowl was played on January 28, 1995.
5. Finally, we can use this information to answer the question. The NFL team that won the Super Bowl in the year Justin Bieber was born is the San Francisco 49ers.
``````output

llama_print_timings:        load time =   434.15 ms
llama_print_timings:      sample time =    41.81 ms /   121 runs   (    0.35 ms per token)
llama_print_timings: prompt eval time =  2523.78 ms /    48 tokens (   52.58 ms per token)
llama_print_timings:        eval time = 23971.57 ms /   121 runs   (  198.11 ms per token)
llama_print_timings:       total time = 28945.95 ms
'\n\n1. First, find out when Justin Bieber was born.\n2. We know that Justin Bieber was born on March 1, 1994.\n3. Next, we need to look up when the Super Bowl was played in that year.\n4. The Super Bowl was played on January 28, 1995.\n5. Finally, we can use this information to answer the question. The NFL team that won the Super Bowl in the year Justin Bieber was born is the San Francisco 49ers.'

GPU​

如果使用 BLAS 后端的安装正确，您将在模型属性中看到 BLAS = 1 指示器。

与 GPU 一起使用的两个最重要的参数是：

  • n_gpu_layers - 确定有多少层模型被卸载到您的 GPU 上。
  • n_batch - 同时处理多少个 tokens。

正确设置这些参数将显著提高评估速度（有关更多详细信息，请参见 wrapper code）。

n_gpu_layers = -1  # The number of layers to put on the GPU. The rest will be on the CPU. If you don't know how many layers there are, you can use -1 to move all to GPU.
n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.

# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)
llm_chain = prompt | llm
question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
llm_chain.invoke({"question": question})


1. Identify Justin Bieber's birth date: Justin Bieber was born on March 1, 1994.

2. Find the Super Bowl winner of that year: The NFL season of 1993 with the Super Bowl being played in January or of 1994.

3. Determine which team won the game: The Dallas Cowboys faced the Buffalo Bills in Super Bowl XXVII on January 31, 1993 (as the year is mis-labelled due to a error). The Dallas Cowboys won this matchup.

So, Justin Bieber was born when the Dallas Cowboys were the reigning NFL Super Bowl.
``````output

llama_print_timings:        load time =   427.63 ms
llama_print_timings:      sample time =   115.85 ms /   164 runs   (    0.71 ms per token,  1415.67 tokens per second)
llama_print_timings: prompt eval time =   427.53 ms /    45 tokens (    9.50 ms per token,   105.26 tokens per second)
llama_print_timings:        eval time =  4526.53 ms /   163 runs   (   27.77 ms per token,    36.01 tokens per second)
llama_print_timings:       total time =  5293.77 ms
"\n\n1. Identify Justin Bieber's birth date: Justin Bieber was born on March 1, 1994.\n\n2. Find the Super Bowl winner of that year: The NFL season of 1993 with the Super Bowl being played in January or of 1994.\n\n3. Determine which team won the game: The Dallas Cowboys faced the Buffalo Bills in Super Bowl XXVII on January 31, 1993 (as the year is mis-labelled due to a error). The Dallas Cowboys won this matchup.\n\nSo, Justin Bieber was born when the Dallas Cowboys were the reigning NFL Super Bowl."

Metal​

如果 Metal 的安装正确，您将在模型属性中看到 NEON = 1 指示器。

两个最重要的 GPU 参数是：

  • n_gpu_layers - 确定有多少层模型被卸载到您的 Metal GPU。
  • n_batch - 并行处理的令牌数量，默认是 8，可以设置为更大的数字。
  • f16_kv - 出于某种原因，Metal 仅支持 True，否则您会遇到错误，例如 Asserting on type 0 GGML_ASSERT: .../ggml-metal.m:706: false && "not implemented"

正确设置这些参数将显著提高评估速度（有关更多详细信息，请参见 wrapper code）。

n_gpu_layers = 1  # The number of layers to put on the GPU. The rest will be on the CPU. If you don't know how many layers there are, you can use -1 to move all to GPU.
n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
)

控制台日志将显示以下日志以指示 Metal 已正确启用。

ggml_metal_init: allocating
ggml_metal_init: using MPS
...

您还可以通过监视进程的 GPU 使用情况来检查 Activity Monitor，开启 n_gpu_layers=1 后 CPU 使用率将显著下降。

对于第一次调用 LLM，性能可能较慢，因为模型在 Metal GPU 中进行编译。

语法​

我们可以使用 语法 来限制模型输出，并根据其中定义的规则采样令牌。

为了演示这个概念，我们包含了 示例语法文件，将在下面的示例中使用。

创建 gbnf 语法文件可能会耗时，但如果您有输出模式重要的用例，有两个工具可以帮助您：

  • 在线语法生成应用 可以将 TypeScript 接口定义转换为 gbnf 文件。
  • Python 脚本 用于将 json schema 转换为 gbnf 文件。例如，您可以创建 pydantic 对象，使用 .schema_json() 方法生成其 JSON schema，然后使用这个脚本将其转换为 gbnf 文件。

在第一个示例中，提供指定的 json.gbnf 文件的路径以生成 JSON：

n_gpu_layers = 1  # The number of layers to put on the GPU. The rest will be on the CPU. If you don't know how many layers there are, you can use -1 to move all to GPU.
n_batch = 512  # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
# Make sure the model path is correct for your system!
llm = LlamaCpp(
    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
    callback_manager=callback_manager,
    verbose=True,  # Verbose is required to pass to the callback manager
    grammar_path="/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/json.gbnf",
)
%%capture captured --no-stdout
result = llm.invoke("Describe a person in JSON format:")
{
  "name": "John Doe",
  "age": 34,
  "": {
    "title": "Software Developer",
    "company": "Google"
  },
  "interests": [
    "Sports",
    "Music",
    "Cooking"
  ],
  "address": {
    "street_number": 123,
    "street_name": "Oak Street",
    "city": "Mountain View",
    "state": "California",
    "postal_code": 94040
  }}
``````output

llama_print_timings:        load time =   357.51 ms
llama_print_timings:      sample time =  1213.30 ms /   144 runs   (    8.43 ms per token,   118.68 tokens per second)
llama_print_timings: prompt eval time =   356.78 ms /     9 tokens (   39.64 ms per token,    25.23 tokens per second)
llama_print_timings:        eval time =  3947.16 ms /   143 runs   (   27.60 ms per token,    36.23 tokens per second)
llama_print_timings:       total time =  5846.21 ms

我们还可以提供 list.gbnf 来返回一个列表：

n_gpu_layers = 1
n_batch = 512
llm = LlamaCpp(
    model_path="/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin",
    n_gpu_layers=n_gpu_layers,
    n_batch=n_batch,
    f16_kv=True,  # MUST set to True, otherwise you will run into problem after a couple of calls
    callback_manager=callback_manager,
    verbose=True,
    grammar_path="/Users/rlm/Desktop/Code/langchain-main/langchain/libs/langchain/langchain/llms/grammars/list.gbnf",
)
%%capture captured --no-stdout
result = llm.invoke("List of top-3 my favourite books:")
["The Catcher in the Rye", "Wuthering Heights", "Anna Karenina"]
``````output

llama_print_timings:        load time =   322.34 ms
llama_print_timings:      sample time =   232.60 ms /    26 runs   (    8.95 ms per token,   111.78 tokens per second)
llama_print_timings: prompt eval time =   321.90 ms /    11 tokens (   29.26 ms per token,    34.17 tokens per second)
llama_print_timings:        eval time =   680.82 ms /    25 runs   (   27.23 ms per token,    36.72 tokens per second)
llama_print_timings:       total time =  1295.27 ms

相关​

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