【Learn in one article】Llama.cpp

Let's first take a look at what functions llama.cpp can achieve and what conveniences it can bring:

• Model inference: Load a pre-trained language model and generate text locally without an internet connection.

• Multi-round conversation: Supports chat mode, retains conversation context, and enables continuous conversation.

• Streaming output: Supports token-by-token streaming responses. (Like ChatGPT)

• Model quantization: supports multiple quantization formats (Q4, Q5, Q8, etc.) to reduce memory usage.

• Multiple sampling strategies: Top-k, Top-p, Temperature, Mirostat and other sampling controls.

• Tokenizer support: Support SentencePiece / BPE tokenizers, automatically loaded.

• Model format support: Supports GGUF model format (recommended), compatible with LLaMA, Mistral, etc.

• Parameter control: supports setting batch size, context length, sampling parameters, etc.

• API service: Provides HTTP / WebSocket mode interface. (Through `llama-server`)

• Multi-platform support: Support Linux, macOS, Windows, iOS, Android, WASM.

• Plugin/Integration: Can be embedded into C/C++ applications, and can also be called by Python/C#/Rust, etc.

• Inference optimization: supports SIMD acceleration (AVX2/NEON), KV cache, and context reuse.

• Local operation: All content can be run offline without the need for an internet connection, protecting privacy. It is suitable for building personal and corporate knowledge bases containing non-public information.

• Prompt simulation: supports simulating ChatGPT prompt mode, which is convenient for connecting to the front end.

• Tool support: Provides quantification tools, model format conversion tools, word segmentation tools, etc.

We select several commonly used functions for analysis:

1. Reasoning and chat function

 Basic reasoning (single round)

Enter the prompt via the command line or API to return the results generated by LLM:

./main -m models/llama-2-7b.Q4_K_M.gguf -p "What is the capital of France?"

Output:

The capital of France is Paris.

Multi-round dialogue (chat mode)

Supports maintaining context and conducting continuous conversations, such as running the following commands:

./chat -m models/llama-2-7b.Q4_K_M.gguf

Sample dialogue:

>  Hello!Assistant: Hi! How can I help you today?
> What 's 12 x 9?Assistant: 12 multiplied by 9 is 108.

2. Model quantization and loading

Supported quantization formats (via the `quantize` tool):

Through quantization, we can compress the original model from tens of GB to a few GB, making it suitable for running on ordinary computers.

Multiple sampling strategies

llama.cpp implements various text sampling strategies to control the randomness and diversity of the output:

• Temperature: controls the creativity of the output. (Higher temperatures are more random)

• Top-k Sampling: Sample from the k tokens with the highest probability.

• Top-p Sampling (nucleus): Sampling from tokens with cumulative probability p.

• Mirostat: An adaptive sampling strategy to control perplexity (smarter).

• Repeat penalty: Reduce the probability of duplicate tokens.

3. API service function (through server module)

`llama.cpp` can be deployed as a local API service using `llama-server` or community projects such as `llama-cpp-python`.

Example:

./server -m models/llama-2-7b.Q4_K_M.gguf

Provide OpenAI-style API interface:

POST /v1/chat/completions{ "messages": [ {"role": "user", "content": "Tell me a joke"} ]}

llama.cpp is a powerful and efficient Large Language Model (LLM) inference engine. Although it does not support training, it has rich functions in inference, including model loading, chat, streaming output, context management, quantization, API interface, etc. It is an indispensable tool for using Meta series models.

If we want to use llama.cpp well, we must first understand the content of this open source framework:

1. User Interface Layer

This layer provides users with different usage modes:

Features:

• Simple and practical

• Support streaming output

• Support parameter control (temperature, top-k, etc.)

2. llama inference engine (llama.cpp / llama.h)

This is the core module of llama.cpp, responsible for building models and executing reasoning logic. It is the user-oriented API layer that encapsulates calls to ggml.

Key features:

Features:

• Good packaging

• Support streaming generation

• Support multiple sampling strategies

3. GGML (computational graph and tensor computing library)

ggml is the underlying computing engine of llama.cpp, which is used to efficiently perform neural network inference on the CPU. It does not rely on external libraries and is implemented entirely in C language.

Features:

Advantages:

• Extremely lightweight

• Native SIMD support

• Can be self-compiled into WebAssembly, iOS, Android and other platforms

4. Models and quantitative tools

Supports converting HuggingFace or original PyTorch models to the format used by llama.cpp (GGUF is recommended).

Tool Introduction:

GGUF format introduction:

• gguf has become a popular model format replacing the old `.bin` format.

• Supports metadata, tokenizer, model structure, and quantization parameters.

• Easier to be compatible across platforms and versions.

According to official information, we summarized the design concept of the llama.cpp architecture:

• Lightweight: does not rely on Python or CUDA, and is implemented only in C/C++

• Cross-platform: Support Linux, macOS, Windows, iOS, Android, WASM

• High performance: SIMD acceleration, quantization support, low memory usage

• Modularity: clear layers and easy to extend (such as plug-in sampling strategies)

Finally, let's summarize the capabilities of llama.cpp:

• Model reasoning: Support local operation and text generation of multiple LLMs

• Chat dialogue: multi-round contextual dialogue, role-playing

• Streaming output: real-time token output

• Efficient operation: support quantization, SIMD, multithreading, and context reuse

• Model tools: model quantification, conversion, analysis

• Local deployment: supports CPU-only, Android, iOS, and other platforms

• API service: can be deployed as a backend interface service

• Multi-language access: support Python / C++ / Rust and other language calls

llama.cpp is known for its high performance, flexibility, and ease of use, and is suitable for both research and production environments. It is not only powerful, but also very friendly to beginners, with rich official documentation and sample codes, which facilitates quick start and secondary development. It is a highly modular and extensible library suitable for natural language processing tasks, covering a variety of functions from basic to advanced, meeting the needs of different scenarios, and providing us with a more convenient tool for building large model applications.

llama.cpp Github: https://github.com/ggml-org/llama.cpp

  --THE END--