62bf73d25c
* Get started with Onyx * Add architecture * Skip keys handled in super() * Loading tensors * Shorten * Graph * Apply suggestion from @pcuenca * Remove norm now embedding in transformers weights * Add eot * Explicit output_multiplier * Handle post_norm_eps * No super call; unhardcode eot. The pattern `self._set_vocab_gpt2()` seems preferred throughout the codebase, and it allows `set_vocab()` to be called from a different part of the Python class hierarchy: the drafter model converter that we may need eventually. * Register for drafting * DFlash: inherit rope type from the linked target. Another option would be to store it in the gguf file itself. * mmproj conversion Note: some fields to be renamed after the implementation works. We are keeping compatibility with the reference Meta gguf for testing purposes. * "clip" header declarations * Load mmproj * Pre-processing * Graph * Go back to using delimiters. Otherwise our generations are worse. Transformers does not use them. We need to trace inputs to verify whether they are equivalent. * downsample_factor -> merge_size * Add vision graph lol, forgot from a previous commit * Additional renames, align with llama.cpp / transformers * Prefer _size instead of independent _h and _w * Fix token layout Co-authored-by: Young Han <younghan@fb.com> * onyx: bring the chat parser onto the onyx branch common/chat.cpp on this branch has no Onyx handling, so a converted model serves malformed chat: the assistant preamble leaks into content ("to=self<|message|>...") and tool calls fail with HTTP 500 "The model produced output that does not match the expected peg-native format" common_chat_params_init_onyx exists on onyx-fair-patch, added there by 8bb73dd3d. It was never on this branch, so this is not a regression -- the two lines developed independently. The code here is taken verbatim from that commit. It is the clean side of `git merge origin/onyx-fair-patch`: chat.cpp is one of the files that merges without conflict. The full merge is not viable -- it produces 13 conflicts, including add/add on conversion/onyx.py and src/models/onyx.cpp where the q_norm-folding and metadata-scale approaches contradict each other, and #4/#7 are stacked on this branch's side of that. Verified on this branch: builds with 0 errors, converts an Onyx checkpoint, and serving it gives "4" for "What is 2+2?" plus a correct get_weather {"city":"Paris"} tool call, where the unported branch gives the two failures above. No converter or runtime changes are included, so this should not interact with the q_norm work. Co-authored-by: Beto de Paola <betodepaola@meta.com> * Less params, bilinear pos-emb interpolation as a graph op instead of CPU * Map to symbolic V_MMPROJ instead of strings * Make a couple params explicit * Patchify via build_inp() * No param for rope_theta * Small cleanup * Restore blank line * Unpermute, to adapt to the latest transformers checkpoint * Apply norm after token embeddings This follows the latest transformers approach. * Remove duplicated function * build_vit * onyx: use the model rope theta on sliding-window layers * DFlash: conversion from transformers drafter * Revert rope_type derivation from target NOTE: this breaks compatibility with Meta's distributed DFlash GGUFs, as the Q/K are stored in "NEOX" (rotated half) format, like in transformers. * Apply suggestion from @pcuenca * Set model type * Remove comment that will become obsolete * Hardcode post_norm_rms_eps instead of new param * Derive SWA+RoPE pattern from gguf array or scalar * Fix model type <-> number of layers * Reorder * Rename * Fix typo * DFlash: seed the draft KV cache from multimodal embedding batches `common_speculative_impl_draft_dflash::process()` returned early on any batch carrying embeddings, so an image prefill never had its target-layer features fused through the DFlash encoder and injected into the draft's KV cache. That left a hole spanning the image's positions, and the next injection at a post-image position failed to initialize its batch: ``` decoding image batch 1/1, n_tokens_batch = 256 decode: failed to initialize batch llama_decode: failed to decode, ret = -1 process: llama_decode(ctx_dft) failed rc=-1 (n_tokens=17, offset=0) srv decode: failed to process speculative batch ``` Every image request with `--spec-type draft-dflash` failed with HTTP 500. Text-only was unaffected, since those batches carry token ids and were let through. Restore the earlier condition, which admits a batch that is either tokens or embeddings and skips only the degenerate neither/both cases. The rest of `process()` is already layout-agnostic -- it gathers features via `llama_get_embeddings_layer_inp()` and indexes `batch_in.pos[]` / `batch_in.seq_id[]`, none of which assume token ids -- so this is the whole fix. Validated against `muse-glimmer-30B-bf16.gguf` + `mmproj-muse-glimmer-30B-bf16.gguf` + a DFlash draft head, on an image describe-the-shapes request: - before: HTTP 500, `failed to process speculative batch` - after: HTTP 200, draft acceptance 0.34012 (167 accepted / 491 generated), mean len 3.04 Output equivalence holds, which is the property that matters: at temperature 0 the drafted response is byte-identical to the same request served with no draft attached (1213/1213 chars), so the draft is drafting correctly through the image context rather than merely not crashing. * Conversion: prefer rewrite to mapping * Revert "Conversion: prefer rewrite to mapping" This reverts commit a92d0ac584d315e876741e85b6dad3dbc8b23bf7. * fix lint * sliding_window metadata is not optional * disable state save/load * Apply suggestion from @pcuenca --------- Co-authored-by: Young Han <younghan@fb.com> Co-authored-by: Beto de Paola <betodepaola@meta.com> Co-authored-by: Daniel Han <michaelhan2050@gmail.com> Co-authored-by: ruanrms <ruanslv@gmail.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / 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