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* server: add read_image tool (#25875) Adds a server-tool that allows vision models to analyze server-side images. This tool is reading a single file for now: The image data is base64 encoded and passed to the UI, which decodes it, fills the <img> tag and removes the data URI before passing the tool result back to the model. * cleanup read_image tool: move magic strings to constants * Add dedicated constants file: tools/ui/src/lib/constants/read-image.ts with PREFIX_IMAGE, PREFIX_SIZE, PREFIX_MIME constants * Use ATTACHMENT_SAVED_REGEX from agentic.ts in ChatMessageToolCallBlockReadImage.svelte * Use NEWLINE constant from code.ts instead of hardcoded '\n' * Use PREFIX_SIZE in regex pattern for size parsing * Add SERVER_TOOL_READ_IMAGE_PREFIX_* constants in C++ server-tools.cpp to match the TypeScript PREFIX_* constants for consistency * server: rename read_image tool to read_media for images and audio * Rename server_tool_read_image to server_tool_read_media in C++ * Rename enum BuiltInTool.READ_IMAGE to READ_MEDIA * Rename UI constants, parser, and Svelte component files * Update display label from 'Read image' to 'Read media' * ui: consolidate audio data URI handling into shared utility * Extract getAudioInputFormat to a shared utility (was duplicated inline) * Store raw base64 in base64Data on the message object * Use base64Data to construct data URIs for audio rendering * Update agentic store to build INPUT_AUDIO parts from base64Data * server: read_media: restrict audio to wav/mp3 and minor fixes * Server get_mime_from_extension now only advertises audio/wav and audio/mpeg (the only formats the model's input_audio API accepts) * Case-insensitive extension matching (fixes .MP3, .Wav, etc.) * Unknown extensions return an error instead of a multi-MB data URI that inflates model context with garbage * Updated tool description to document supported formats * Frontend AUDIO_MIME_TO_EXTENSION trimmed to match server * fix a missing import in tools/ui/src/lib/stores/agentic.svelte.ts * server: read_media: add to --tools help text and README tool list * ui: fix indentation in ChatMessageToolCallBlockDefault.svelte * server: read_media tool: fix a cast to use the correct type * server: read_media: multiple fixes * server-tools.cpp import cctype, remove UTF-8 char, check mime before reading file * ui: add MimeTypePrefix.AUDIO and use it in agentic.svelte.ts * server: make read_media inherit from read_file and add uses_cwd * ui: fix formating issues * rm from server * move it to frontend-only tool * correct partial commit * rm unused * ui: address review from allozaur Replace the magic strings, regexes and number in the read_media parser and service with named constants. Path splitting reuses FILE_PATH_SEPARATOR_REGEX, the size header regex moves to READ_MEDIA_SIZE_REGEX derived from PREFIX_SIZE, and FILE_EXTENSION_SEPARATOR lands next to it in constants/code.ts. --------- Co-authored-by: ckrafft <ckrafft@epyc> Co-authored-by: Xuan Son Nguyen <son@huggingface.co> Co-authored-by: Pascal <admin@serveurperso.com>
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