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9a7570587c
Problem
- Loader prefers `<arch>.attention.recurrent_layers`, falls back to `full_attention_interval` if missing
- Converter only ever writes the interval. gguf-py has no constant/writer for the array
- Interval can only describe evenly spaced full-attention layers. Any non-uniform `layer_types` gets reconstructed wrong
- No error, no warning. Model loads, runs, wrong layers get wrong ops. Full-attn layers marked recurrent lose their KV cache
- Every published Qwen3.5 checkpoint is uniform so nobody's hit it yet
Repro
12 layers, periods 4/3/5:
layer: 0 1 2 3 4 5 6 7 8 9 10 11
actual: L L L F L L F L L L L F
loader: L L L F L L L F L L L F
^ ^
Layer 6 is full attn, loaded as recurrent. Layer 7 the reverse.
52-layer non-uniform stack: 15/52 mis-typed.
Fix
- `constants.py`: add `Keys.Attention.RECURRENT_LAYERS` (name already registered in llama-arch.cpp)
- `gguf_writer.py`: add `add_recurrent_layers()`, same shape as `add_rope_pattern()`
- `conversion/qwen.py`: emit array from `layer_types` in `Qwen3NextModel.set_gguf_parameters` (covers 3-Next, 3.5, 3.5-MoE)
Notes
- Array is padded with `false` for MTP blocks. `get_key_or_arr` checks length against `n_layer_all`, which includes MTP. Matches the fallback's `i < n_layer()` guard
- Interval is still written. Old builds only understand the interval
- `layer_types` length != `num_hidden_layers` now raises in converter instead of producing a GGUF that fails at load
Tested
- End-to-end on a 62-layer non-uniform Qwen3.8-27B (2 linear layers removed). Loader reads the array, 62 blocks, 0 mismatches. Without fix: interval fallback, mis-typed
- MTP padding NOT tested on a real MTP model. Reasoned from qwen35.cpp + get_key_or_arr. Would appreciate a check
Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
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
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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 | 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