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* fix(gallery): repoint qwen3-4b/qwen3.5-9b dflash drafters at post-rename GGUFs The drafters both entries referenced were converted from the pre-merge DFlash PR branch and carry dflash.target_layer_ids. llama.cpp reads dflash.target_layers and refuses the load. The stored values are offset by +1 relative to the HF-side field, so the files cannot be repaired by renaming the key and must be replaced. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): add apexentries HuggingFace client Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): build the HF client via pkg/httpclient The apexentries HuggingFace client was constructed as a raw &http.Client{Timeout: 60s}. The repo convention (documented in .golangci.yml, which cannot express this as a forbidigo pattern) is that all outbound HTTP goes through pkg/httpclient, which refuses redirects by default and sets a TLS 1.2 floor. The std client follows redirects and forwards custom credential headers to the redirect target on a cross-host hop (GHSA-3mj3-57v2-4636). Only a User-Agent is sent today, but this calls an external API and an HF_TOKEN header added later would leak. Switch to httpclient.NewWithTimeout, preserving the 60 second timeout. No behaviour change for the current header set. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): discover APEX tiers by filename suffix Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): resolve unsloth counterparts and sharded quants Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): render APEX child entries with the dflash/mtp tag rule Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): set backend, known_usecases and cross-repo drafters RenderChild left three gaps against the hand-written gallery entries. The generated entries reference gallery/virtual.yaml, which supplies no backend, so every generated entry named no engine at all. All comparable hand-written entries set backend: llama-cpp in overrides; do the same. Set known_usecases to [chat] alongside it: LocalAI falls back to the backend defaults when it is absent, so this is convention rather than breakage, but generated entries should not read differently from their neighbours. The drafter was also assumed to live in the repo publishing the weights. Speculative pairings routinely cross repos, and a drafter URI built from the weights repo 404s at install time. Add ChildInput.DraftRepo, used for both the drafter URI and its local path, falling back to Repo when empty so pairings that do ship the drafter alongside the weights are unchanged. The dflash/mtp tagging rule is untouched: the tag still follows SpecType and nothing else. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): dedupe generated entries against the existing gallery Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * apexentries: canonicalize HF URIs and dedup the generated batch Merge exists to stop a second gallery entry being added for weights the gallery already ships, but two gaps let duplicates through on a bulk run. The URI key was compared as an exact string while render.go only ever emits https://huggingface.co/{repo}/resolve/main/{file} and the gallery records 1038 of its URIs in huggingface://{repo}/{file} shorthand. A generated unsloth rung whose weights are already shipped in shorthand was therefore not recognised. canonicalURI reduces both spellings to one key and is applied on both sides, taking care that the repo is exactly the first two path segments so sharded quants in a subdirectory still match. A URI in neither form is returned untouched so other hosts dedup on their literal string. Merge also never accounted for entries it had just accepted, so two generated entries sharing a name or a primary URI both landed in add. Several APEX repos share one base model and resolve to the same unsloth counterpart, so the identical rungs are generated twice under the same name. Batch state is tracked locally rather than written back into the caller's ExistingIndex, which a caller may reasonably reuse. Name is still checked before URI: a name collision must block the add regardless of the weights. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): verify variant and tagging invariants in the gallery index Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(ci): scope the apex-entries verifier to what it can actually judge The verifier reported 60 problems against the real gallery, 57 of which were llama.cpp assumptions meeting entries from other backends. A gate that is wrong 57 times out of 60 cannot gate anything. - The weight-count check catches a quant label collision in llama-cpp quant discovery, so it now runs only for overrides.backend: llama-cpp. Entries with no declared backend are skipped because their weights are declared in the referenced url: template, which the verifier never reads. - The dflash/mtp tag check now implements the per-backend table in .agents/adding-gallery-models.md instead of assuming llama.cpp's spec_type: vocabulary. ds4 declares mtp_path:/mtp_draft:; sglang declares speculative_algorithm: in a file this verifier cannot follow, so sglang entries are not judged in either direction. The check stays bidirectional within the backends it does judge. - sha256 is now required on .gguf files only, since every non-GGUF asset in the index belongs to a hand-curated entry outside this generator's scope. Against the current gallery this leaves exactly the three genuine problems: two entries setting spec_type:draft-mtp without the mtp tag, and one entry whose overrides.mmproj names a file it does not download. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): anchor quant matching and invert the sha256 rule UnaccountedQuants matched files to wanted quants with strings.Contains, which reproduces the substring collision it was written to warn about: Q8_0 is a substring of UD-Q8_0, so a repo publishing only UD-Q8_0 was reported as publishing an unbuilt Q8_0. Subdirectory-sharded UD quants are the normal unsloth layout for large repos, so this fired on realistic input. Match on the quant label as an anchored token instead, the way DiscoverUnslothQuants does, so the diagnostic and the discovery it audits cannot disagree about what a file is. Root-level shards, the layout the diagnostic mainly exists to catch, stay detected. The sha256 requirement was scoped to .gguf, which exempted seven real model weights: wan_2.1_vae.safetensors and clip_vision_h.safetensors across the wan-2.1-*-ggml entries, both load-bearing weights named by gallery/wan-ggml.yaml. Invert the rule so a checksum is required on everything except metadata extensions, which keeps a future weight format covered by default rather than silently exempt. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): wire the apexentries command Adds the generation path to the apexentries command: list the mudler APEX repos, discover each one's quality ladder and its unsloth counterpart's quant rungs from the filenames actually published, render a child entry per build plus a family parent carrying the variants list, dedup against the gallery, and write the additions to -out or append them with -apply. Discovery shortfalls are reported at discovery time rather than left to the verifier. A quant or a tier that discovery drops leaves no trace in a finished gallery file, and because an empty imatrix ladder falls back to the plain one, a repo whose imatrix filenames all fail to match downgrades the whole family silently instead of erroring. Merge's single reused map is split into two reported categories. A URI match means the gallery already ships exactly these weights and referencing the existing entry is correct; a name collision means an unrelated entry owns the name and referencing it would substitute a different build. Multimodal children now declare known_usecases [chat, vision]. An explicit known_usecases suppresses the backend-default fallback, so a chat-only entry carrying an mmproj never matches the vision or multimodal gallery filters. .github/ci is invisible to go list ./..., so a workflow names both generator packages explicitly and their specs finally run on pull requests. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(ci): gather APEX builds under the base model entry The hub for a family is the BASE model entry, never a generated *-apex parent. Somebody looking for qwen3.6-35b-a3b has to find every build of those weights under that one name, so a competing qwen3.6-35b-a3b-apex hub would split the family and leave half of it invisible. When the gallery already ships the base entry, a variants block is spliced into it textually, leaving its description, icon, tags, overrides and files untouched. Only a family whose base model the gallery does not ship gets a new hub, still named for the base model and carrying one of the discovered builds as its own payload so it declares a backend the verifier can judge. The line editing is factored into .github/ci/galleryedit, shared with the variantproposals job, so the two cannot drift apart on where a variants block belongs. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(apexentries): treat an unreadable optional counterpart repo as absent HuggingFace answers 401 Unauthorized, not 404, for a repository that does not exist when the request carries no credentials. FetchRepoFiles treated only 404 as absence, so probing for the OPTIONAL unsloth counterpart hard failed for every family that legitimately has none: 27 of the 45 APEX families are community merges that will never have an unsloth build, and a full run failed all of them. Split the fetch so the two call sites can apply different policies to the same response. The APEX repo itself stays strict: a 401 or 403 on a repo the run requires is a real failure and still errors. Only the optional probe tolerates it, because without a token 401 cannot be told apart from absence. That collapse is lossy in one direction, since a private or gated repo also answers 401, so the skipped candidates are named in the run summary alongside the other silent-shortfall counters instead of being dropped in silence. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(apexentries): report full-precision sources as a known exclusion The 45 APEX repos publish their unquantized F16 sources next to the imatrix ladder, flat or sharded. Discovery correctly emits nothing for them, but they were landing in the unclassified total, leaving a permanent baseline of 24 benign lines on every run. That baseline is what the unclassified check exists to prevent: a standing count of known-benign files is exactly what hides the one file that ever genuinely matters. Count full-precision sources separately and give them their own summary line, so unclassified returns to 0 and stays loud when something really is an unknown shape. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(apexentries): namespace local paths by owner and enable MTP builds localPath namespaced downloads by the repo basename alone, so two repos publishing the same filename under different owners collapsed to one local path. LiquidAI/LFM2.5-8B-A1B-GGUF and unsloth/LFM2.5-8B-A1B-GGUF collided that way, and both were offered from the same hub, so installing the second either overwrote the first model's weights or was skipped as already present while recording a sha256 that did not match the bytes on disk. The owner is now its own path segment: owner/repo is globally unique on HuggingFace and neither half can contain a separator, so uniqueness holds by construction. Verify gains a check for the whole class, that no local filename may map to two different upstream URIs. It surfaces seven pre-existing collisions in the gallery, which are left alone here. Entries built from the *-APEX-MTP-GGUF repos now configure MTP rather than shipping the heads inert, matching the pattern the hand-written MTP entries already use: spec_type:draft-mtp with spec_n_max and spec_p_min, tagged mtp, and no draft_model because the heads live in the weights. RenderChild no longer requires a separate drafter file before it will configure a spec type, while the cross-repo drafter path is unchanged. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(gallery): add the APEX GGUF families as variant ladders Adds the imatrix quality ladder from each mudler/*-APEX-GGUF repo, a fixed subset of unsloth quant rungs where a counterpart repo exists, and the MTP builds, then attaches them to the base model entry so one entry offers every build of the same weights and LocalAI picks the one that fits the hardware. Ten existing base model entries gain a variants list; twenty-seven families that the gallery had no base entry for get one. Builds are discovered from the filenames each repo actually publishes rather than derived from its name, since six repos ship a stem that differs from their repo name. Every file carries a sha256 taken from the HuggingFace API. Assisted-by: Claude Opus 4.8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.
A small core, not a bundle. Each backend wraps a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX...) in its own image, pulled only when a model needs it. You install nothing you don't use.
- Composable by design: backends are separate and pulled on demand, so you install only what your model needs
- Open and extensible: load any model, or build your own backend in any language against an open interface
- Drop-in API compatibility: OpenAI, Anthropic, and ElevenLabs APIs across every backend
- Any model, any modality: LLMs, vision, voice, image, and video behind one API
- Any hardware: NVIDIA, AMD, Intel, Apple Silicon, Vulkan, or CPU-only
- Multi-user ready: API key auth, user quotas, role-based access
- Built-in AI agents: autonomous agents with tool use, RAG, MCP, and skills
- Privacy-first: your data never leaves your infrastructure
Created by Ettore Di Giacinto and maintained by the LocalAI team.
📖 Documentation | 💬 Discord | 💻 Quickstart | 🖼️ Models | ❓FAQ
Guided tour
https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18
Click to see more!
User and auth
https://github.com/user-attachments/assets/228fa9ad-81a3-4d43-bfb9-31557e14a36c
Agents
https://github.com/user-attachments/assets/6270b331-e21d-4087-a540-6290006b381a
Usage metrics per user
https://github.com/user-attachments/assets/cbb03379-23b4-4e3d-bd26-d152f057007f
Fine-tuning and Quantization
https://github.com/user-attachments/assets/5ba4ace9-d3df-4795-b7d4-b0b404ea71ee
WebRTC
https://github.com/user-attachments/assets/ed88e34c-fed3-4b83-8a67-4716a9feeb7b
Quickstart
macOS
Note: The DMG is not signed by Apple. After installing, run:
sudo xattr -d com.apple.quarantine /Applications/LocalAI.app. See #6268 for details.
Containers (Docker, podman, ...)
Already ran LocalAI before? Use
docker start -i local-aito restart an existing container.
CPU only:
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
NVIDIA GPU:
# CUDA 13
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13
# CUDA 12
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
# NVIDIA Jetson ARM64 (CUDA 12, for AGX Orin and similar)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
# NVIDIA Jetson ARM64 (CUDA 13, for DGX Spark)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13
AMD GPU (ROCm):
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
Intel GPU (oneAPI):
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
Vulkan GPU:
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
Loading models
# From the model gallery (see available models with `local-ai models list` or at https://models.localai.io)
local-ai run llama-3.2-1b-instruct:q4_k_m
# From Huggingface
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
# From the Ollama OCI registry
local-ai run ollama://gemma:2b
# From a YAML config
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
# From a standard OCI registry (e.g., Docker Hub)
local-ai run oci://localai/phi-2:latest
To test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, /models lists installed models and /model <name> switches between them.
# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m
# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m
Automatic Backend Detection: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see GPU Acceleration.
For more details, see the Getting Started guide.
Latest News
- June 2026: New native biometric backends from the LocalAI team: voice-detect.cpp for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and face-detect.cpp for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python
insightfaceandspeaker-recognitionbackends (PR #10441). - June 2026: New realtime voice assistant demo (a tiny Go client for the Realtime API with a full talk-back voice loop and tool calling), plus streaming of the realtime LLM / TTS / transcription pipeline stages and configurable WebRTC ICE candidates.
- June 2026: Big speech push: the parakeet.cpp ASR engine gains NeMo-faithful segment timestamps, a multilingual streaming Nemotron-3.5 model, dynamic batching for concurrent transcription and CUDA graphs; the new CrispASR backend adds multi-architecture ASR + TTS, and 60 Piper TTS voices across 42 languages land in the gallery (plus per-request TTS instructions and params).
- June 2026: New backends and models: locate-anything.cpp for open-vocabulary object detection via ggml, Ideogram4 image generation in stablediffusion-ggml, llama.cpp video input, and the Gemma 4 QAT family with MTP speculative-decoding pairs. Plus an interactive CLI chat mode and RAG source citations in agent responses.
- June 2026: Distributed mode hardening: prefix-cache-aware routing, a production-ready request router with auto-sized embedding/rerank batches, ds4 layer-split distributed inference, NATS JWT auth + TLS/mTLS, and resumable file uploads.
- May 2026: LocalAI 4.3.0 -
llama.cppprompt cache on by default (repeated system prompts collapse from minutes to seconds), keyless cosign signing of backend OCI images, per-API-key + per-user usage attribution, Distributed v3 with per-request replica routing. Release notes - May 2026: LocalAI 4.2.0 - LocalAI sees and hears: voice recognition, face recognition + antispoofing liveness, speaker diarization. Plus drop-in Ollama API, video generation, redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. Release notes
- April 2026: LocalAI 4.1.0 - LocalAI becomes a control tower: distributed cluster mode with VRAM-aware smart routing + autoscaling, multi-user platform with OIDC and API keys, per-user quotas with predictive analytics, in-UI fine-tuning with TRL (auto-export to GGUF), on-the-fly quantization backend, visual pipeline editor. Release notes
- March 2026: LocalAI 4.0.0 - native agentic orchestration with the new Agenthub community hub, full React UI rewrite with Canvas mode, MCP Apps + client-side with tool streaming, WebRTC realtime audio, MLX-distributed. Release notes
- February 2026: Realtime API for audio-to-audio with tool calling, ACE-Step 1.5 support
- January 2026: LocalAI 3.10.0 — Anthropic API support, Open Responses API, video & image generation (LTX-2), unified GPU backends, tool streaming, Moonshine, Pocket-TTS. Release notes
- December 2025: Dynamic Memory Resource reclaimer, Automatic multi-GPU model fitting (llama.cpp), Vibevoice backend
- November 2025: Import models via URL, Multiple chats and history
- October 2025: Model Context Protocol (MCP) support for agentic capabilities
- September 2025: New Launcher for macOS and Linux, extended backend support for Mac and Nvidia L4T, MLX-Audio, WAN 2.2
- August 2025: MLX, MLX-VLM, Diffusers, llama.cpp now supported on Apple Silicon
- July 2025: All backends migrated outside the main binary — lightweight, modular architecture
For older news and full release notes, see GitHub Releases and the News page.
Features
- Text generation (
llama.cpp,transformers,vllm... and more) - Text to Audio
- Audio to Text
- Image generation
- OpenAI-compatible tools API
- Realtime API (Speech-to-speech)
- Embeddings generation
- Constrained grammars
- Download models from Huggingface
- Vision API
- Object Detection
- Reranker API
- P2P Inferencing
- Distributed Mode — Horizontal scaling with PostgreSQL + NATS
- Model Context Protocol (MCP)
- Built-in Agents — Autonomous AI agents with tool use, RAG, skills, SSE streaming, and Agent Hub
- Backend Gallery — Install/remove backends on the fly via OCI images
- Voice Activity Detection (Silero-VAD)
- Integrated WebUI
Supported Backends & Acceleration
LocalAI supports 60+ backends including llama.cpp, vLLM, SGLang, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for NVIDIA (CUDA 12/13), AMD (ROCm), Intel (oneAPI/SYCL), Apple Silicon (Metal), Vulkan, and NVIDIA Jetson (L4T). All backends can be installed on-the-fly from the Backend Gallery.
See the full Backend & Model Compatibility Table and GPU Acceleration guide.
Backends built by us
Most backends wrap a best-in-class upstream engine. A handful of them are native C/C++/GGML engines (no Python at inference) developed and maintained by the LocalAI project itself:
| Backend | What it does |
|---|---|
| parakeet.cpp | C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription |
| moss-transcribe.cpp | C++/GGML port of OpenMOSS MOSS-Transcribe-Diarize: joint long-form transcription, speaker diarization and timestamping in a single pass |
| moss-tts.cpp | C++/GGML port of the OpenMOSS MOSS-TTS family: text-to-speech (MOSS-TTS-Local v1.5, 48 kHz stereo) with reference-audio voice cloning, through the MOSS-Audio-Tokenizer neural codec |
| ced.cpp | C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition |
| voice-detect.cpp | Speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion), replacing the Python speaker-recognition backend |
| voxtral-tts.c | Voxtral Realtime 4B speech-to-text in pure C |
| vibevoice.cpp | Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization |
| rf-detr.cpp | Native RF-DETR object detection and instance segmentation |
| locate-anything.cpp | Open-vocabulary object detection and visual grounding (LocateAnything-3B) |
| depth-anything.cpp | Depth Anything 3 monocular metric depth + camera pose estimation |
| face-detect.cpp | Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend |
| free-splatter.cpp | Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required |
| privacy-filter.cpp | Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier |
| LocalVQE | Joint acoustic echo cancellation, noise suppression, and dereverberation |
| local-store | Local-first vector database for embeddings (shipped in-tree) |
We also maintain apex-quant, a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.
Resources
- Documentation
- LLM fine-tuning guide
- Build from source
- Kubernetes installation
- Integrations & community projects
- Installation video walkthrough
- Media & blog posts
- Examples — including the realtime voice assistant demo (Go client for the Realtime API with tool calling)
Team
LocalAI is maintained by a small team of humans, together with the wider community of contributors.
- Ettore Di Giacinto — original author and project lead
- Richard Palethorpe — maintainer
A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in Discord — LocalAI is a community-driven project and wouldn't exist without you. See the full contributors list.
Citation
If you utilize this repository, data in a downstream project, please consider citing it with:
@misc{localai,
author = {Ettore Di Giacinto},
title = {LocalAI: The free, Open source OpenAI alternative},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/go-skynet/LocalAI}},
Sponsors
Do you find LocalAI useful?
Support the project by becoming a backer or sponsor. Your logo will show up here with a link to your website.
A huge thank you to our generous sponsors who support this project covering CI expenses, and our Sponsor list:
Past sponsors
Individual sponsors
A special thanks to individual sponsors, a full list is on GitHub and buymeacoffee. Special shout out to drikster80 for being generous. Thank you everyone!
Star history
License
LocalAI is a community-driven project created by Ettore Di Giacinto and maintained by the LocalAI team.
MIT - Author Ettore Di Giacinto mudler@localai.io
Acknowledgements
LocalAI couldn't have been built without the help of great software already available from the community. Thank you!
- llama.cpp
- https://github.com/tatsu-lab/stanford_alpaca
- https://github.com/cornelk/llama-go for the initial ideas
- https://github.com/antimatter15/alpaca.cpp
- https://github.com/EdVince/Stable-Diffusion-NCNN
- https://github.com/ggerganov/whisper.cpp
- https://github.com/rhasspy/piper
- exo for the MLX distributed auto-parallel sharding implementation
Contributors
This is a community project, a special thanks to our contributors!