mirror of
https://github.com/LostRuins/koboldcpp.git
synced 2026-09-02 02:51:17 +02:00
Daniel Bevenius
dac869b0a0
This commit contains a fix for the conversion of NVIDIA Nemotron 3.5 Lightning which currently incorrectly converts when using a transformers version later than 5.5.1. When converting using [convert](https://github.com/ggml-org/convert) the transformers version is 5.13.1 and this produces the following: ```console WARNING:gguf.gguf_writer:Duplicated key name 'nemotron_h_moe.attention.head_count_kv', overwriting it with new value [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] of type ARRAY ``` This does not happen with transformers 5.5.1. The reason seems to be that the configuration is different in later versions, for example when using 5.13.1 the configuration block looks like this: ```console transformers 5.13.1 raw has layers_block_type: True autoconfig has layers_block_type: True autoconfig layers_block_type: [ 'linear_attention', 'moe', 'linear_attention', 'moe', 'linear_attention', 'full_attention', 'moe', ... ] ``` And with 5.5.1 we get: ```console transformers 5.5.1 raw has layers_block_type: True autoconfig has layers_block_type: True autoconfig layers_block_type: [ 'mamba', 'moe', 'mamba', 'moe', 'mamba', 'attention', 'moe' ... ] ``` In our conversion script we only match for attention, not full attention which is causing this issue. With the changes in this commit the output with transformers 5.13.1 will be: ```console (venv) $ gguf-dump models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf | grep head_count_kv INFO:gguf-dump:* Loading: models/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16.gguf 29: [INT32] | 52 | nemotron_h_moe.attention.head_count_kv = [0, 0, 0, 0, 0, 2, ...] ``` Resolves: https://github.com/ggml-org/llama.cpp/issues/27718 Refs: https://github.com/ggml-org/convert/actions/runs/32949047680/job/98116096069#step:5:2391
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 [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
- 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