Roadmap / Project status / Manifesto / ggml

Inference of Meta's LLaMA model (and others) in pure C/C++

• Changelog for libllama API
• Changelog for llama-server REST API

• Introducing GGUF-my-LoRA ggml-org#10123
• Hugging Face Inference Endpoints now support GGUF out of the box! ggml-org#9669
• Hugging Face GGUF editor: discussion | tool

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

• Plain C/C++ implementation without any dependencies
• Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
• AVX, AVX2, AVX512 and AMX support for x86 architectures
• 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
• Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads MTT GPUs via MUSA)
• Vulkan and SYCL backend support
• CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is the main playground for developing new features for the ggml library.

The main product of this project is the llama library. Its C-style interface can be found in include/llama.h. The project also includes many example programs and tools using the llama library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server. Possible methods for obtaining the binaries:

• Clone this repository and build locally, see how to build
• On MacOS or Linux, install llama.cpp via brew, flox or nix
• Use a Docker image, see documentation for Docker
• Download pre-built binaries from releases

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

• Trending
• LLaMA

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

• Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
• Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: ggml-org#10123)
• Use the GGUF-editor space to edit GGUF meta data in the browser (more info: ggml-org#9268)
• Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: ggml-org#9669)

To learn more about model quantization, read this documentation

• Run simple text completion

  llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128
  
  # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.

• Run in conversation mode

  llama-cli -m model.gguf -p "You are a helpful assistant" -cnv
  
  # > hi, who are you?
  # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
  #
  # > what is 1+1?
  # Easy peasy! The answer to 1+1 is... 2!

• Run with custom chat template

  # use the "chatml" template
  llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --chat-template chatml
  
  # use a custom template
  llama-cli -m model.gguf -p "You are a helpful assistant" -cnv --in-prefix 'User: ' --reverse-prompt 'User:'

  Supported templates

• Constrain the output with a custom grammar

  llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
  
  # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}

  The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

  For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

• Start a local HTTP server with default configuration on port 8080

  llama-server -m model.gguf --port 8080
  
  # Basic web UI can be accessed via browser: http://localhost:8080
  # Chat completion endpoint: http://localhost:8080/v1/chat/completions

• Support multiple-users and parallel decoding

  # up to 4 concurrent requests, each with 4096 max context
  llama-server -m model.gguf -c 16384 -np 4

• Enable speculative decoding

  # the draft.gguf model should be a small variant of the target model.gguf
  llama-server -m model.gguf -md draft.gguf

• Serve an embedding model

  # use the /embedding endpoint
  llama-server -m model.gguf --embedding --pooling cls -ub 8192

• Serve a reranking model

  # use the /reranking endpoint
  llama-server -m model.gguf --reranking

• Constrain all outputs with a grammar

  # custom grammar
  llama-server -m model.gguf --grammar-file grammar.gbnf
  
  # JSON
  llama-server -m model.gguf --grammar-file grammars/json.gbnf

• Measure the perplexity over a text file

  llama-perplexity -m model.gguf -f file.txt
  
  # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
  # Final estimate: PPL = 5.4007 +/- 0.67339

• Measure KL divergence

  # TODO

• Run default benchmark

  llama-bench -m model.gguf
  
  # Output:
  # | model               |       size |     params | backend    | threads |          test |                  t/s |
  # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
  # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
  # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
  #
  # build: 3e0ba0e60 (4229)

• Run a model with a specific prompt (by default it's pulled from Ollama registry)

  llama-run granite-code

• Basic text completion

  llama-simple -m model.gguf
  
  # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of

• Contributors can open PRs
• Collaborators can push to branches in the llama.cpp repo and merge PRs into the master branch
• Collaborators will be invited based on contributions
• Any help with managing issues, PRs and projects is very appreciated!
• See good first issues for tasks suitable for first contributions
• Read the CONTRIBUTING.md for more information
• Make sure to read this: Inference at the edge
• A bit of backstory for those who are interested: Changelog podcast

• main (cli)
• server
• GBNF grammars

• How to build
• Running on Docker
• Build on Android
• Performance troubleshooting
• GGML tips & tricks

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

• LLaMA:
  • Introducing LLaMA: A foundational, 65-billion-parameter large language model
  • LLaMA: Open and Efficient Foundation Language Models
• GPT-3
  • Language Models are Few-Shot Learners
• GPT-3.5 / InstructGPT / ChatGPT:
  • Aligning language models to follow instructions
  • Training language models to follow instructions with human feedback