mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-28 17:07:31 -05:00
Georgi Gerganov
d77dd0806d
* tests : use llama_context_ptr in test-recurrent-state-rollback Replace raw llama_context pointers with llama_context_ptr and drop the manual llama_free calls and cleanup lambda. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL * tests : run test-recurrent-state-rollback over all dummy models Add a --models DIR mode that mirrors test-save-load-state: iterate every dummy model, report PASS/FAIL/SKIP in a table and fail only when a model fails. Register a single ctest entry with ARGS --models instead of the four per-model registrations. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL * cont : fix typo * metal : allow fusing 0-element nodes to keep graph packing shape-independent The fusion packing in ggml_metal_fusion_max excluded 0-element tensors and the topk_moe/moe_reduce checks rejected n_tokens == 0, so graphs decoding batches with no outputs packed differently from the worst-case reserved graph. The Metal optimizer then reordered the nodes differently and ggml_gallocr_needs_realloc failed on the layout mismatch, forcing an unexpected graph re-reserve (caught by GGML_SCHED_DEBUG_REALLOC). Treat empty tensors like their non-empty counterparts: match them in the pattern sequence and only reject genuinely malformed shapes. Fused kernels dispatch zero threadgroups for empty graphs, which is a legal no-op. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL * tests : run test_multi_seq_split_replay as a separate test test_multi_seq_split_replay was invoked at the end of test_rollback, so its result was folded into the rollback status and it only ran when the rollback part passed. Give it its own test_status return, run both tests independently over both cache fills via a shared run_tests helper, and report them as separate rollback / split replay columns in the --models table with per-test summaries. The exit code fails when either test fails. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL * tests : loosen the split replay nmse bound to 1e-4 test-generate-models seeds its weights from std::random_device, and some generated lfm2 models drift up to ~1.7e-5 nmse on the split replay due to rounding noise, tripping the previous 1e-5 bound. Raise the bound to 1e-4 so the random generations stop flaking. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL * tests : reuse run_tests_for_model in single-model mode The single-model path duplicated the model init and the non-recurrent check from run_tests_for_model; route it through the shared helper instead. Model load failures now return FAIL rather than SKIP so that --model with a broken file still exits non-zero, and the helper loads with model_only like the --models loop does since the tests create their own contexts. Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL
llama.cpp
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / 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
|
|
|
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 | 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
- Release process
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 - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain