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* sampling: enhance penalty handling in common_sampler_init - Set default value for penalty_last_n based on model context if not specified. - Ensure penalty_last_n and n_prev are non-negative. - Update llama_sampler_penalties structure to inherit from llama_sampler_backend and add backend input handling for penalties. - Implement backend initialization and application logic for penalties, including frequency and presence adjustments. * tests: add backend penalties sampling tests and utility functions - Introduced `accept_prompt` and `unique_prompt_tokens` functions to handle prompt acceptance and token uniqueness. - Implemented `compare_penalties_logits` to compare logits from backend and CPU samplers with penalties. - Added `test_backend_penalties_sampling` to validate backend penalties with various configurations. - Enhanced the test suite for better coverage of penalty handling in sampling. * sampling: add support for top-k penalties in backend sampling * sampling: add fix to ensure stable numerical results. Preserve masked logits as -Inf and no longer generate NaN. * sampling: enhance penalty comparison tests with masking penalties logic * add comments on padding * sampling: add comments on modifications * add the unit test to cover masked-out token as -INF * validate repeat penalty to ensure it is finite and greater than 0; add tests for invalid values * refactor: test functions to share logic and be less verbose * add test to cover case where previously penalized token is not part of candidates * remove comments * remove redundant penalty_last_n initialization and validation in common_sampler_init * add support for penalties in sampler chain with configurable positions * add validation for penalty parameters and enhance tests for non-finite values * add context parameter to common_sampler_init and set default for penalty_last_n * add llama_n_ctx parameter to common_sampler_init for improved sampler initialization * replace penalty_last_n x n_candidates comparison matrix with a vocabulary-sized count tensor * add tests for backend penalties sampling without filler entries , token_count.size() == n_active == n_max == 64 * add test for backend penalties sampling after top-p with large history window * remove as unused * add is_disabled method, tensor logits reshape, add rest review suggestions * clarify comment
llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / dev branches / compile times / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
# Download and run a model directly from Hugging Face
llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF
# Launch OpenAI-compatible API server
llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
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Description
The main goal of llama.cpp is to enable LLM (and VLM) 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
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V 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 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 build on top of the ggml library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon [In Progress] | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cpprepo and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
- subprocess.h - Single-header process launching solution for C and C++ - Public domain