Core AI model zoo
Models
Core AI is Apple’s on-device ML runtime in iOS 27 / macOS 27 and the successor to Core ML: PyTorch models are exported with Apple’s coreai-torch (LLMs: coreai.llm.export) into .aimodel bundles that run on the GPU or the Neural Engine, e.g. Qwen3-8B 4-bit decodes at 94 tok/s on an M4 Max GPU, MLX 90 under the same protocol (apple-silicon-llm-bench, macOS 27 beta 26A5353q, 2026-06-11).
Converted models + conversion recipes for Apple Core AI (.aimodel, iOS 27 / macOS 27):
every model here is downloadable, gated against the original model before it ships, and carries
the recipe that produced it in models/<model>/recipe.toml — rerun it
(zoo_convert.py run <name>), check the published bundle against its source model
(zoo_verify.py), or adapt it for your own port. Don’t take the gates on faith — they are run
here, and everything needed to re-run them is published. Each card states what was measured on
which hardware (iPhone tier is device-measured; the large models are Mac-only and say so) and how
strong that model’s parity proof is, because it differs per model.
Where the shipped configuration could not be recovered from the repository, the recipe says so
rather than guessing. Model ports are open to everyone — the catalog serves
community ports from the contributor’s own Hugging Face namespace, credited by name
(who).
Successor to CoreML-Models.
| Model | Download (.aimodel) |
Run in app | License |
|---|---|---|---|
| Qwen3.5-0.8B | 🤗 qwen3.5-0.8B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Qwen3.5-2B | 🤗 qwen3.5-2B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Qwen3.6-35B-A3B (MoE, Mac-only) | 🤗 Qwen3.6-35B-A3B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Qwen3.6-27B (dense, Mac-only) | 🤗 Qwen3.6-27B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Qwen3.8-27B (dense VLM, Mac-only — the Qwen3.8 generation’s only open compact model, text ported on release day + full vision path: fp16 ViT tower, embeddings-input mRoPE decoder, S=16 chunked prefill at 86 tok/s) | 🤗 Qwen3.8-27B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Ornith-1.0-9B (zoo’s first agentic-coding model — self-scaffolding coder, Qwen3.5 arch, DeepReinforce; Mac-only, 48 tok/s int8 / 59 int4) | 🤗 Ornith-1.0-9B-CoreAI | CoreAIChatMac | MIT |
| GLM-4.7-Flash (MoE + MLA, Mac-only — zoo’s first MLA) | 🤗 GLM-4.7-Flash-CoreAI | ChatDemo ↗ | MIT |
| 🔧 Gemma 4 E2B (text, incl. official-QAT int4) | 🤗 gemma-4-E2B-CoreAI | ChatDemo ↗ | Gemma |
| Gemma 4 E2B ⚡raw-Metal (hand-written mixed-bit int2/int4 kernels, ~55 tok/s on iPhone 17 Pro = LiteRT-LM parity, lossless) | raw-metal/ in 🤗 gemma-4-E2B-CoreAI |
CoreAIChat | Gemma |
| 🔧 Gemma 4 E4B (text, official-QAT int4) | 🤗 gemma-4-E4B-CoreAI | ChatDemo ↗ | Gemma |
| 🔧 Gemma 4 12B (dense, Mac-only — custom flash-decode kernel) | 🤗 Gemma-4-12B-CoreAI | ChatDemo ↗ | Gemma |
| 🔧 Gemma 4 31B (dense, Mac-only — custom flash-decode kernel) | 🤗 Gemma-4-31B-CoreAI | ChatDemo ↗ | Gemma |
| LFM2.5-1.2B-Instruct | 🤗 LFM2.5-1.2B-CoreAI | ChatDemo ↗ | LFM Open License v1.0 |
| LFM2.5-2.6B (reasoning; int4lin clears the cliff this family usually hits) | 🤗 LFM2.5-2.6B-CoreAI | ChatDemo ↗ | LFM Open License v1.0 |
LFM2.5-8B-A1B (MoE, custom gather_qmm kernel — first iPhone MoE) |
🤗 LFM2.5-8B-A1B-CoreAI | ChatDemo ↗ | LFM Open License v1.0 |
LFM3-1.7B (reasoning, tool calls in XML, LiquidAI — 13 short-conv + 11 gated attention layers, 5 of them on a 256-token sliding window; Mac: int8 with a 64-token prefill function beside main in one asset, 169.5 tok/s decode and the first token in 0.83 s from a 1,024-token prompt on M4 Max on the kit’s path; iPhone 18 Pro: a static Neural Engine bundle — 8-bit palettized, AOT h19p, 47.2 tok/s decode, a 768 + 256 turn in 6.71 s) |
🤗 LFM3-1.7B-CoreAI | lfm3-1.7b in CoreAIKit’s catalog on main (not yet in a release) |
LFM Open License v1.0 |
| Granite 4.0-H 1B / 350M | 🤗 granite-4.0-h-CoreAI | ChatDemo ↗ (1B) · 350M: CoreAIChat | Apache-2.0 |
| Nanbeige4.1-3B (dense reasoning/agentic, iPhone — 32B-class @ 3.93B) | 🤗 Nanbeige4.1-3B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Nanbeige4.2-3B (looped Llama: 22 physical blocks × 2 passes, 44 KV layers; int8 46.4 tok/s on M4 Max; port by Vadim Smirnov) | 🤗 Nanbeige4.2-3B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| MiniCPM5-1B (1B-class on-device LLM, hybrid Think/No-Think, 128K, OpenBMB; GPU int8 and a Neural Engine bundle — stock static iOS export, 8-bit palettized, AOT h18p, device gate 3/3) | 🤗 MiniCPM5-1B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| MiniCPM5-2B (2B-class on-device LLM, the 1B’s 42-layer sibling — hybrid Think/No-Think, tool calling, 128K; released 2026-09-06, OpenBMB; GPU int8 and a Neural Engine bundle — Apple’s stock static iOS export, 4-bit palettized, AOT h18p, device gate 3/3) | 🤗 MiniCPM5-2B-CoreAI | ChatDemo ↗ | Apache-2.0 |
| Youtu-LLM-2B (dense MLA — zoo’s first iPhone MLA & first dense MLA; DeepSeek-V2-style latent-KV attention at 1.96B with an absorbed flash-decode kernel, reasoning + agentic; Tencent) | 🤗 Youtu-LLM-2B-CoreAI | ChatDemo ↗ | Other (youtu-llm) |
| BitCPM-8B (zoo’s first 1.58-bit ternary LLM — every weight is {-1,0,+1}; MiniCPM4-8B arch, custom 2-bit packed-GEMM Metal kernel; 8B running in ~2.1 GB on iPhone GPU; OpenBMB) | 🤗 BitCPM-8B-CoreAI | CoreAIChat ‡ | Apache-2.0 |
| Bonsai 2 27B (PrismML’s ternary Qwen3.8-27B — group-128 trits in a blockwise-Hadamard basis, read straight from the PQ2_0 GGUF; the 64-layer GDN hybrid on custom Metal kernels, 23.6 tok/s decode on an M4 Pro at ~7.5–8 GB; Mac, text-only; port by Rahul Rachuri) | 🤗 ternary-bonsai-2-27b-coreai | bonsai-swift ↗ (kit enrollment pending) | Apache-2.0 |
LLaDA-8B dLLM (zoo’s first diffusion LLM — masked-diffusion decode: fills a canvas of [MASK] tokens in parallel, not left-to-right AR; bidirectional LLaMA-dense 8B, d3LLM-distilled; int4 ~4.9 GB, Mac) |
🤗 LLaDA-8B-dLLM-CoreAI | DiffuseChat ↗ | Other |
| BitVLA (zoo’s first Vision-Language-Action / robotics model + first ternary multimodal — image+instruction → 7-DoF robot action; 1.58-bit BitNet-2B LLM + BitSigLIP vision, shared ternary kernel; runs on iPhone GPU; arXiv 2506.07530) | 🤗 BitVLA-CoreAI | CoreAIChat ‡ | MIT |
| Qwen3-VL (vision-language) | 🤗 2B · 4B · 8B | VLChat ↗ | Apache-2.0 |
| Holo2-4B (GUI-grounding / computer-use VLM — screenshot + instruction → click coordinates; Qwen3-VL-4B backbone, H Company; zoo’s first computer-use model) | 🤗 Holo2-4B-CoreAI | VLChat ↗ | Apache-2.0 |
| MiniCPM-V 4.6 (vision-language, sub-2B — strongest tiny VLM) | 🤗 MiniCPM-V-4.6-CoreAI | VLChat ↗ | Apache-2.0 |
| MiniCPM-V 4.7-1B (vision-language, 1B — 4.6’s tower with canvas M-RoPE derived in the graph; iPhone 18 Pro 98.0 tok/s decode) | 🤗 MiniCPM-V-4.7-1B-CoreAI | VLChat (coreai-kit #72) · Look ↗ | Apache-2.0 |
| WeVisOne-VL-2B (vision-language with a pixel decoder, Tencent — boxes, counts, points, OCR and alpha mattes of what you name in a photo; built upon Qwen3-VL, plus a 9.5M-parameter pixel decoder, as four graphs; iPhone 18 Pro 0.99–1.00 s per cut-out) | 🤗 WeVisOne-VL-2B-CoreAI | Cutout ↗ (coreai-kit #79) | Apache-2.0 |
| LFM2.5-VL-450M (vision-language, 658 MB — the smallest VLM here; SigLIP2-NaFlex + the shipped LFM2 decoder, 112 tok/s on iPhone 17 Pro) | 🤗 LFM2.5-VL-450M-CoreAI | VLChat ↗ | LFM Open License v1.0 |
| North-Micro-Vision (vision-language, 2.4B, 11 languages incl. Japanese — Cohere; iPhone 24/24 token-exact vs fp32) | 🤗 North-Micro-Vision-CoreAI | VLChat ↗ | Apache-2.0 |
| LFM2.5-VL-3B (vision-language, Mac-only — the detail tier of the same family; int4 costs it nothing, unlike the 450M) | 🤗 LFM2.5-VL-3B-CoreAI | VLChat ↗ | LFM Open License v1.0 |
Shieldstral-1.0-3B (safety classifier — you write the policy in plain language at call time; ships as ONE forward → probs[1,2], no decode loop; 12 languages, 123.6 ms/verdict on a Mac and 371.9 ms on an iPhone 17 Pro, 9/9 vs fp32 at int4 on both) |
🤗 Shieldstral-CoreAI | — | Apache-2.0 |
| 🔧 Gemma 4 E2B vision (VL) (image+text) | vl/ in 🤗 gemma-4-E2B-CoreAI |
CoreAIChat | Gemma |
| Unlimited-OCR (document OCR → markdown: tables→HTML, formulas→LaTeX; zoo’s first doc-OCR — stock runtime, no patch, flat-latency R-SWA) | 🤗 Unlimited-OCR-CoreAI | ReadDoc ↗ | MIT |
| GLM-OCR (document OCR → Markdown; GLM-4.V small 0.9B, single-pass, tables→Markdown; iPhone + Mac, ~4 s/page) | 🤗 GLM-OCR-CoreAI | ReadDoc ↗ | MIT |
MinerU2.5-Pro (whole-page document parsing → structured Markdown; zoo’s first whole-page auto-structuring — 2-stage layout + per-region recognition in one stock Qwen2-VL 1.2B, tables→<table> HTML; Mac) |
🤗 MinerU2.5-Pro-CoreAI | ReadDoc ↗ | Apache-2.0 |
| Qwen2.5-Omni-3B Audio (audio understanding — describes sounds, not a transcript; iPhone + Mac, zoo’s first audio model) | 🤗 Qwen2.5-Omni-3B-Audio-CoreAI | AudioChat ↗ | Apache-2.0 |
| Whisper large-v3-turbo (speech→text — 100 languages, auto-detect; stock runtime, iPhone AOT + Mac) | 🤗 whisper-large-v3-turbo-CoreAI-official | Transcribe ↗ | MIT |
| Qwen3-ASR-1.7B (speech→text — the zoo’s first ASR; AuT encoder + Qwen3 decoder, 52 languages; iPhone + Mac) | 🤗 Qwen3-ASR-1.7B-CoreAI | Transcribe ↗ | Apache-2.0 |
| Fun-ASR-Nano-2512 (speech→text — Chinese with 7 dialects incl. Cantonese, English, Japanese; hotword list in the prompt; SAN-M encoder + fine-tuned Qwen3-0.6B, 1.2 GB; 150/155 token-exact vs the fp32 oracle, rest knife-edge; iPhone 18 Pro RTF 0.075, M4 Max 0.022) | 🤗 Fun-ASR-Nano-2512-CoreAI | FunASRGate (kit enrollment pending) | Apache-2.0 |
| EmbeddingGemma 2 Transcribe (speech→text, English — the audio encoder of Google’s EmbeddingGemma 2, frozen, and a 19.6 M-parameter reader trained on the 960 h of LibriSpeech that reads each 40 ms token as the closest of 200,000 words or blank; greedy CTC, no language model; fp16 encoder + fp16 reader with its word table, 30 s windows; test-clean WER 4.21 %; M4 Max 54-min recording in 6.33 s (×515) with the reader on the Neural Engine, iPhone 18 Pro encoder 59.6 ms per 30 s window) | 🤗 EmbeddingGemma-2-Transcribe-CoreAI (reader v2 upload pending) | SwiftUI app in the eg2-transcribe repository (kit enrollment pending) | Apache-2.0 |
| Parakeet-TDT-0.6B (speech→text — zoo’s first transducer / TDT (RNN-T); NVIDIA FastConformer + LSTM predictor + joint, 3 graphs + host greedy loop, 25 EU languages; iPhone 47.9× real-time) | 🤗 Parakeet-TDT-0.6B-CoreAI | Transcribe ↗ | CC-BY-4.0 |
| Parakeet-TDT-0.6B-v2 (speech→text — the English-only sibling of the v3 port; same FastConformer TDT, vocab 1025; iPhone 17 Pro Max 175.3× real-time over 66 min of audio at 20376/20376 tokens exact; port by Rahul Rachuri) | 🤗 parakeet-tdt-0.6b-v2-coreai | parakeet-swift ↗ (kit enrollment pending) | CC-BY-4.0 |
| Nemotron 3.5 ASR Streaming 0.6B (speech→text — the zoo’s first STREAMING ASR: live-mic transcription in 320 ms chunks, cache-aware FastConformer + pure RNN-T with explicit KV/conv cache I/O; 40 locales in one checkpoint via a run-time language input, punctuation built in, any-length audio) | 🤗 Nemotron-3.5-ASR-Streaming-CoreAI | coreai-audio | OpenMDW-1.1 |
| Streaming Sortformer 4-spk v2 (speaker diarization — the zoo’s first “who spoke when”, up to 4 speakers; NeMo core as a Core AI graph + Swift host streaming loop + AOSC speaker-cache compression; pairs with any zoo ASR for a diarized transcript; iPhone + Mac, 100% activity-agree vs NeMo) | 🤗 Streaming-Sortformer-Diar-CoreAI | coreai-audio | CC-BY-4.0 |
| Nemotron-3-Diarization (speaker diarization, the 8-speaker successor of the row above: “who spoke when” for up to 8 speakers at 10 ms, streaming in three latency modes or offline; NVIDIA’s streaming Sortformer as one Core AI graph + a Swift host whose speaker cache follows transformers line by line; Mac GPU and iPhone 17 Pro GPU 99.9987 % activity-agree vs transformers fp32 on 97.6 s, 30 ms per 0.72 s chunk on the iPhone) | 🤗 Nemotron-3-Diarization-CoreAI | — (Swift host: NemotronDiarizer) | OpenMDW-1.1 |
| S1-mini by Superwhisper (ASR text normalizer — the zoo’s first post-ASR model: raw transcript → clean written text, fillers and false starts resolved, punctuation and casing applied, spoken numbers/dates/currency/emails written out; a Qwen3-0.6B finetune on the stock graph, steered by a control line. Completes the dictation path behind Parakeet / Nemotron-ASR, all on-device; iPhone 17 Pro 62.4 tok/s, 276/276 token-exact, chunk input to ~450–500 tok) | 🤗 S1-mini-CoreAI | — (kit enrollment pending) | Apache-2.0 + naming clause |
| Kokoro-82M (text-to-speech — zoo’s first TTS; StyleTTS2 + iSTFTNet, 28 English voices, runs on any text) | 🤗 Kokoro-82M-CoreAI | Speak ↗ | Apache-2.0 |
| VoxCPM-0.5B (text-to-speech — diffusion TTS: MiniCPM4 LM + LocDiT flow-matching + AudioVAE; iPhone + Mac, int8 LM) | 🤗 VoxCPM-0.5B-CoreAI | Speak ↗ | Apache-2.0 |
| VoxCPM2 2B (text-to-speech — 2B successor at 48 kHz: MiniCPM4 28L LM + LocDiT-12L flow-matching + 48 kHz AudioVAE; iPhone + Mac, int8 LM) | 🤗 VoxCPM2-CoreAI | Speak ↗ | Apache-2.0 |
| VibeVoice-Realtime-0.5B (text → multi-speaker dialogue — the zoo’s first multi-speaker / podcast-style TTS: dual Qwen2.5 LM (4L context + 20L speech) + next-token diffusion head (DPMSolver++ v-pred, CFG) + causal-conv acoustic VAE, 24 kHz; host turn-switching for N-speaker conversations, pairs with Sortformer for a generate → diarize loop; iPhone + Mac, all-fp16, 10.6 tok/s ≈ 1.4× real-time on iPhone 17 Pro) | 🤗 VibeVoice-Realtime-0.5B-CoreAI | coreai-audio | MIT |
| pocket-tts (text-to-speech — Kyutai’s 109M streaming TTS, the zoo’s first Kyutai model and first Mimi conversion: AR flow-matching LM over Mimi latents + one-step flow decoder + streaming Mimi decoder, 8 voices, takes text directly so there is no G2P layer to degrade; iPhone 17 Pro Max 7.8× real-time at 169 MB peak; port by Rahul Rachuri) | 🤗 pocket-tts-coreai | pocket-tts-swift ↗ (kit enrollment pending) | CC-BY-4.0 |
| Audio8-TTS-Preview-0.6b (text-to-speech — 11 languages incl. ja / zh / yue / en / ko, zero-shot voice cloning from a 0.5–30 s reference; DualAR of the Fish Audio S2 Pro design: Qwen2.5-shaped slow AR + 4-layer fast AR + 44.1 kHz DAC-style codec, 601M + codec; the zoo’s first semantic-token TTS and first sampler inside the graph — one call per frame; int8 slow AR + fp16 fast AR 876 MB + codec 261 MB (+ 416 MB encoder for voice registration); ASR round trip ja CER 2.3 % / en WER 0.9 % / zh 0 % vs the oracle’s 6.5 / 0.0 / 0.0; iPhone 18 Pro RTF 0.92 whole utterance / 1.5 streaming (device JIT, load 6.1 s → 0.5 s), M4 Max 0.70 / 0.81) | 🤗 Audio8-TTS-Preview-0.6b-CoreAI | Audio8Gate (kit enrollment pending) | Apache-2.0 |
| Mel-Band RoFormer (song → vocals + instrumental stems — the zoo’s first source separation: band-split mel RoFormer with STFT/iSTFT folded into the graph as constant DFT matmuls, so the host only frames and overlap-adds; iPhone + Mac, fp16, 6.5× real-time on iPhone 17 Pro) | 🤗 MelBandRoformer-Vocal-CoreAI | coreai-audio | MIT |
| Stable Audio Open Small (text → music / audio — the zoo’s first generative audio; latent diffusion: T5 encoder + DiT (8-step rectified-flow) + Oobleck VAE, ~11s 44.1 kHz stereo; fp16 ~1 GB, ~0.4 s / 11 s on M4 Max ≈ 30× real-time; Stability AI + Arm) | 🤗 Stable-Audio-Open-Small-CoreAI | Music ↗ | Stability Community |
| V-JEPA 2 (ViT-L, SSv2 — the zoo’s first world model: Meta’s self-supervised video encoder (JEPA, predicts in representation space) + action-recognition head, 174 physical-interaction classes; 16-frame clip → action, fp16 ~675 MB, ~160 ms/clip on M4 Max; Meta AI, MIT) | 🤗 VJEPA2-ViTL-SSv2-CoreAI | ActionCamera ↗ | MIT |
| EmbeddingGemma 300M (text embeddings — on-device RAG / semantic search) | 🤗 embeddinggemma-300m-CoreAI | DocChat ↗ | Gemma |
| Qwen3-Embedding 0.6B (multilingual text embeddings, last-token pooling + MRL) | 🤗 Qwen3-Embedding-0.6B-CoreAI | DocChat ↗ | Apache-2.0 |
| Qwen3-Reranker 0.6B (cross-encoder reranker — yes/no relevance score) | 🤗 Qwen3-Reranker-0.6B-CoreAI | DocChat ↗ | Apache-2.0 |
| Granite-Embedding-97M-Multilingual-R2 (multilingual text embeddings, IBM — the zoo’s smallest embedder (97M, 390 MB fp32) and first ModernBERT encoder: 384-d CLS unit vectors, S=128/512, iPhone 17 Pro AOT 5.5 / 21.0 ms per embedding, 35/35 vs HF on device; optional w8 305 MB) | 🤗 Granite-Embedding-97M-Multilingual-R2-CoreAI | host tokenizer in the repo (kit enrollment pending — pad/SEP/ignore_merges contract) |
Apache-2.0 |
| decider-0.8b (System One decision model, Mapika — typed Choice / Score / Noul questions answered by calibrated option-letter probabilities in one pass, no text generation; the zoo’s first decision model: probability parity vs the author’s fp32 readout max |Δp| 0.0084 on 44 rows, argmax 44/44, M4 Max decode 193.6 tok/s) | 🤗 decider-0.8b-CoreAI | readout gates in conversion/decider/; in CoreAIKit as decider-0.8b (CoreAI.decide) |
Apache-2.0 |
| decider-2b-vision (image-input decision model, Mapika — an image (photo, diagram, game frame) plus lettered questions answered by option-letter probabilities in one pass, no text generation; a fixed-grid tower at 256×256 / 448×448 + an ids-input Qwen3.5 hybrid decoder with M-RoPE in the graph; vs the author’s fp32 readout max |Δp| 0.0094 on 157 fixture slots and 0.0146 on 500 held-out photo runs, argmax equal on all; M4 Max 0.32 s per 256×256 decision in Swift, iPhone 18 Pro 0.75 s (device JIT, 108/108 = the Mac’s)) | 🤗 decider-2b-vision-CoreAI | DeciderVision (Swift package + Mac CLI) + DeciderVisionGate (iPhone / Mac gate); in CoreAIKit as decider-2b-vision (CoreAI.decide(image:…)) |
Apache-2.0 |
clef-flash (typed-decision model, Cloudflare — a state (text, JSON or an image) plus noul / choice / score questions answered with a probability for every option in one prefill, no text generation, in the SystemOne-compatible request shape; a 9B Qwen3.5 hybrid decoder that returns hidden rows + the author’s joint schema head as its own graph + the untied lm_head as a host table, fixed-grid towers at 256×256 / 448×448; vs the author’s fp32 readout max |Δp| 0.0112 on 401 fixture questions and 0.0085 on 188 held-out ones, argmax equal on every question with a margin above 0.02; M4 Max 0.95 s per 436-token decision in Swift; Mac only) |
🤗 clef-flash-CoreAI | ClefFlash (Swift package + Mac CLI); in CoreAIKit as clef-flash (KitClefDecider) |
Apache-2.0 |
Kev-0.8B (typed-decision model, Jared Palmer — a state (text or JSON) plus noul / choice / score questions answered with a probability for every option, one row per question, no text generation, in the SystemOne-compatible request shape; a rank-16 LoRA merged into Qwen3.5-0.8B-Base as a decoder that returns hidden rows, 128 tokens a call with an fp32 Metal kernel for the linear-attention recurrence, + the author’s pointer head on the host; vs the author’s fp32 readout max |Δp| 0.0124 on 434 fixture questions and 0.0058 on 130 held-out ones, argmax equal on every question with a margin above 0.02; M4 Max 29.9 ms per 94-token question in Swift, iPhone 18 Pro 37.8 ms (device JIT, default memory limit, 420/420)) |
🤗 Kev-0.8B-CoreAI | Kev (Swift package + Mac CLI) + KevGate (iPhone / Mac gate); in CoreAIKit as kev-0.8b (KitKevDecider) |
Apache-2.0 |
Kev-4B (typed-decision model, Jared Palmer — the same readout on Qwen3.5-4B-Base: a state plus noul / choice / score questions, a probability for every option, no text generation; 128 tokens a call with an fp32 Metal kernel for the linear-attention recurrence; vs the author’s fp32 readout max |Δp| 0.0153 on 434 fixture questions and 0.0096 on 130 held-out ones, argmax equal on every question with a margin above 0.02; M4 Max 100.2 ms per 94-token question in Swift; Mac only) |
🤗 Kev-4B-CoreAI | Kev (Swift package + Mac CLI); in CoreAIKit as kev-4b (KitKevDecider) |
Apache-2.0 |
d1-3B (typed-decision model with pictures, Liquid AI — a state (text, JSON or pictures) plus noul / choice / score questions answered with a probability for every option, one row per question, no text generation, in the System One request shape; the LFM2.5-VL decoder returning hidden rows at 64 tokens a call, fp16 on the Mac and with the MLP linears int8 on the iPhone, the tied rows of the option tokens on the host, and a SigLIP2 tower per crop; vs the provider’s fp32 readout max |Δp| 0.0039 (fp16) / 0.0197 (int8mlp) on 393 fixture questions, 0.0140 on 120 held-out ones and 0.00049 / 0.0024 on 24 picture questions, argmax equal on every question with a margin above 0.02; M4 Max 34.3 ms per question in Swift (the fp16 .aimodel specialized on the Mac), iPhone 18 Pro 48.0 ms (device JIT, increased-memory-limit, 416/416)) |
🤗 d1-3B-CoreAI | D1 (Swift package + Mac CLI) + D1Gate (iPhone / Mac gate) + D1Demo (the iPhone demo app); in CoreAIKit as d1-3b (KitD1Decider, /v1/systemone with images) |
LFM Open License v1.0 |
| OpenThai-SystemOne (Thai + English System One decision model, iapp — Choice / Score / Noul through a 256-way slot head, no text generation; int8lin 1.07 GB, fp32-oracle option argmax 50/50, max |Δp| 0.020813; M4 Max S=1 prefill proxy 252.7 tok/s pipelined / 197.1 sequential) | 🤗 OpenThai-SystemOne-CoreAI | coreai-kit sequential: tokens / slots / option argmax 50/50; SemIf authored144 mean family balanced accuracy 0.7249 |
Apache-2.0 |
| laya multilingual (encoder-type System One decision model, Convai Innovations — mmBERT-base 322M with a typed decision head, Choice / Score / Noul read at option markers in one forward pass, no prefill, no text generation; wfp16 645 MB (fp16 weights, fp32 compute), official-model argmax 81/81 and max |Δp| 4.5e-6 on 201 rows × 2 windows; M4 Max GPU 12.1 / 19.5 ms per question at S=256 / 512, iPhone not yet measured) | 🤗 Laya-Multilingual-CoreAI | coreai-kit encoder backend (Decision.Format.encoder): tokens / markers 201/201, argmax 81/81, 11.5 ms per decision on the Mac GPU; SemIf authored144 mean family balanced accuracy 0.6114 (the model’s own) |
Apache-2.0 |
| Julia-1 (encoder-type decision model, Supersonic Labs — mmBERT-small 144M with a typed decision head, the laya design at half the width; Choice / Score / Noul read at option markers in one forward pass, no text generation; fp32 582 MB, S=512 / 1024; the bundle’s argmax gives the publisher’s typed-decisions CPU totals 426/542/483, and all 2,120 rows at S=1024 match the publisher’s model within max |Δp| 8.6e-5; M4 Max GPU 8.3 / 16.0 ms per question, the Neural Engine not used, iPhone not measured) | 🤗 Julia-1-CoreAI | Python and Swift reference hosts in conversion/julia/ (not a CoreAIKit catalog model) |
Apache-2.0 |
d1-omni-600M (decision model with an image and an audio tower, Liquid AI — a bidirectional LFM2.5-Encoder-350M trunk with a typed decision head plus a SigLIP2 vision tower and a 17-layer FastConformer audio tower, 587M; a state (text or JSON, with images or one voice clip) plus noul / choice / score questions, a probability for every option read at option markers in one forward pass, no text generation; three fp16 graphs: the decision graph at seven static lengths 64–4,096, vision per crop, audio per 5 / 10 / 20 / 30 s bucket; vs the publisher’s fp32 model max |Δp| ≤ 0.0117 and mean ≤ 0.0011 on every bucket, argmax equal on every question with a margin above 0.02; M4 Max GPU 7.35 ms per short question at L64, 38.3 ms with a 384 px image, 24.9 ms with 9.6 s of audio; iPhone 18 Pro 10.8 / 57.7 / 27.0 ms, parity 78/78 on the device) |
🤗 d1-omni-600M-CoreAI | D1Omni (Swift package + Mac CLI, text / image / audio) + D1OmniGate (iPhone / Mac gate); Python reference host in conversion/d1_omni/; in CoreAIKit as d1-omni-600m (KitD1OmniDecider, /v1/systemone with images or audio) |
LFM Open License v1.0 |
| APUS-Decision-v1-4B (Qwen3.5-4B decision model, apus-ailab — browser actions / workflow routing / proposition judgments through A–P letter probabilities; LM head retained; int8hu 5.77 GB, fp32-oracle option argmax 48/48, max |Δp| 0.005302; M4 Max S=1 prefill proxy 81.8 tok/s pipelined / 69.4 sequential) | 🤗 APUS-Decision-v1-4B-CoreAI | Measured through coreai-kit sequential: tokens / slots / option argmax 40/40 choice+noul; SemIf authored144 mean family balanced accuracy 0.906 | Apache-2.0 |
| Qwen3.5-2B-Decision (source chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-MLX-bf16; English Choice / Bool / Score from space-prefixed letter probabilities; int8hu 3.05 GB, fp16 reference 3.79 GB; Mac GPU argmax 58/58, max |Δp|0.007586 int8hu /0.005788 fp16; S=1 prefill proxy 164.1 / 130.0 tok/s, contended=False) | 🤗 Qwen3.5-2B-Decision-CoreAI | Measured through coreai-kit: tokens/slots/argmax 58/58 both; SemIf authored144 mean family balanced accuracy 0.798 | Apache-2.0 |
| System One scorer — Qwen3.5-4B (source pngwn/system-one-qwen3.5-4b-scorer; per-option scalar Choice / noul / Score, T=1.75; int8lin 5.10 GB, fp16 reference 8.44 GB; Mac GPU argmax 48/48 both, max |Δp|0.010874 int8lin /0.003684 fp16; S=1 prefill proxy 89.347 / 76.039 tok/s, contended=false) | 🤗 system-one-qwen3.5-4b-scorer-CoreAI | measured through coreai-kit: tokens/slots 280/280 and question argmax 48/48 both; int8lin max |Δp| 0.0110, 1,016 ms/question (contended); SemIf authored144 mean family balanced accuracy 0.844; kit catalog entry pending the Hub revision | cc-by-nc-4.0 |
| ColModernVBERT (visual document retrieval — late-interaction MaxSim over page images, no OCR; zoo’s first multi-vector retriever) | 🤗 ColModernVBERT-CoreAI | DocSearch ↗ | MIT |
| GLiNER2-PII (information extraction / NER — the zoo’s first NER / schema-driven extraction & first DeBERTa-v3 port; zero-shot PII detection + redaction, any label set at call time; mDeBERTa-v3 fused graph + Swift host collator/decode, iPhone + Mac, byte-identical to GLiNER2) | 🤗 GLiNER2-PII-CoreAI | InfoExtract ↗ | Apache-2.0 |
GLiNER2.5-Decide (zero-shot text classification, Fastino — name the labels at call time and get a probability for each: an intent, a queue, a severity, a yes/no, several tasks about one text in one forward pass; the DeBERTa-v3-large encoder and label head as one fp16 graph at S = 256 / 512, the schema layout and softmax / sigmoid in the host; decisions equal the fp32 gliner2 library’s on all 787 decisions over 454 texts, on the Mac GPU and the iPhone 18 Pro GPU, 37.8 / 92.9 ms per call there at S = 256 / 512) |
🤗 GLiNER2.5-Decide-CoreAI (upload pending) | coreai-kit TextClassifier (branch gliner-decide): token ids 454/454, decisions 787/787 on the Mac GPU |
Apache-2.0 |
| RF-DETR nano/small/medium/large (object detection, no NMS) | 🤗 RF-DETR-CoreAI | DetectCamera ↗ | Apache-2.0 |
| RF-DETR-Seg nano→2xlarge (instance segmentation, 6 sizes) | 🤗 RF-DETR-CoreAI | DetectCamera ↗ | Apache-2.0 |
| YOLOX-S (object detection — dense anchor-free, host NMS) | 🤗 YOLOX-CoreAI | DetectCamera ↗ | Apache-2.0 |
| AdcSR ×4 (super-resolution — zoo’s first; one-step diffusion-GAN, on-device) | 🤗 AdcSR-CoreAI | UpscaleDemo ↗ | Apache-2.0 + OpenRAIL++ |
| Depth Anything 3 (monocular depth — zoo’s first depth model; small + base, fp16/fp32) | 🤗 Depth-Anything-3-CoreAI | DepthCamera ↗ | Apache-2.0 |
TripoSplat (single image → 3D Gaussian splats — the zoo’s first 3D; DINOv3 ViT-H + 20-step flow-matching DiT + octree sampler + Gaussian decoder, Mac GPU ~1 min; .ply/.splat → RealityKit / MetalSplatter; VAST) |
🤗 TripoSplat-CoreAI | TripoSplatMac | MIT |
| LTX-Video 2B distilled (text → video — the zoo’s first video model; T5-XXL + 8-step flow-matching DiT + causal video VAE, host FlowMatch sampler; 512×768×49f ~14 s Mac GPU; Lightricks) | 🤗 LTX-Video-2B-CoreAI | CoreAIVideo | Other (LTXV) |
| FLUX.2 klein 4B (text → image + in-context editing — the zoo’s first image-generation & editing model; step-distilled flow-matching DiT (4 steps, guidance 1.0) + Qwen3 text encoder, 1024²; native in-context edit — add/replace/combine while keeping the subject, unlike strength-based SDEdit — plus multi-reference compose, both exported as edit-sequence transformers (output latent T=0 concatenated with reference tokens T=10·i) in the 2026-07 fork era; since 2026-10-10 the app runs the stock apple/coreai-models 97a14be: fp16 (10.9 s per 1024² image on M4 Max) and int8 (11.6 s) folders, Edit = the stock one-reference image-to-image (24.4 s), the fork-era edit transformers were removed from the HF main on 2026-10-10 (still at revision 216f2841); Mac; Black Forest Labs) | 🤗 FLUX.2-klein-4B-CoreAI | CoreAIImageGen | Apache-2.0 |
| GLM-Image (text → image — the zoo’s first AR + diffusion hybrid; a 9B GLM-4 AR model samples the image as discrete visual prior tokens like an LLM (~36 tok/s), then a 7B flow-matching DiT denoises conditioned on them + 16ch VAE; composition from the AR, texture from the DiT; 1024² native + 512² fast, int8, Mac; ZhipuAI) | 🤗 GLM-Image-CoreAI | CoreAIImageGen | MIT |
| Z-Image-Turbo (text → image — a 6B Single-Stream DiT (S3-DiT): Qwen3-4B text encoder → 34-block DiT (8-step FlowMatchEuler + CFG) → 16ch VAE; photoreal by default. One graph covers 256²/512²/1024² and any prompt length (dynamic image + caption axes, ~5–9 % cost). bf16 and near-lossless — PSNR 42.6 dB vs the fp32 reference; 18 s @512² / 70 s @1024² on M4 Max. fp16 NaNs this model, so Mac-only: AOT will not take a bf16 module; Alibaba Tongyi-MAI) | 🤗 Z-Image-Turbo-CoreAI | CoreAIImageGen | Apache-2.0 |
RGBA-Image-2.1 (text → RGBA image — the Core AI port of Qwen-Image-2.1, Built with Qwen: the Qwen3-VL-8B text path (bf16 weights, fp32 compute) → a 7B single-stream block-causal DiT (40 FlowMatch Euler steps, no CFG, bf16) → a 64ch VAE with four output channels, so a prompt that asks for a transparent background gets a real alpha. 256² / 512² / 1024² from one DiT graph (dynamic text + image axes); 46.72 / 35.49 dB vs the fp32 reference at 256² / 512², the 40 DiT steps at 1024² in 190 s on M4 Max. Mac-only, runs after an --expect-frequent-reshapes AOT compile; research / evaluation use only; Alibaba Qwen) |
🤗 RGBA-Image-2.1-CoreAI | — (Python engine: pipeline_engine.py) | Qwen Research (non-commercial) |
RGBA-Image-2.1-Turbo (text → RGBA image — the Core AI port of Qwen-Image-2.1-Turbo, Built with Qwen: the distilled checkpoint of the same 7B block-causal DiT, 8 FlowMatch Euler steps on the checkpoint’s own sigma grid, no CFG, bf16 or int8 (the same speed); RGBA-Image-2.1’s text encoder and fp32 VAE bundles, whose weights the Turbo checkpoint shares (the text encoder byte for byte, the VAE as a bf16 copy). 256² / 512² / 1024² from one DiT graph; 43.79 / 42.45 / 42.50 dB vs the fp32 Turbo reference, the 8 DiT steps at 1024² in 37.8 s on M4 Max. Mac-only, runs after an --expect-frequent-reshapes AOT compile; research / evaluation use only; Alibaba Qwen) |
🤗 RGBA-Image-2.1-Turbo-CoreAI | — (Python engine: pipeline_engine.py --turbo) |
Qwen Research (non-commercial) |
| TimesFM 2.5 200M (time-series forecasting — the zoo’s first forecasting foundation model; decoder-only patched transformer, any univariate series → 128-step point + 10-quantile forecast; one stateless graph + host RevIN/flip DSP, fp16 ~463 MB, ~14 ms/forecast M4 Max / ~25 ms iPhone 17 Pro device-verified; Google) | 🤗 TimesFM-2.5-200M-CoreAI | Forecast ↗ | Apache-2.0 |
| Timestral-209M (time-series forecasting, Mistral AI — a patch transformer with time and variate attention; one univariate series → 21 quantiles for up to 1,024 steps from up to 8,192 steps of history, the released forecaster’s symmetric averaging and refine frame in the host; Neural Engine graphs, 9.44 ms per forecast on the iPhone 18 Pro and 9.46 ms on the M4 Max from a 2,048-step history; every shipped graph within 1 % of the released forecaster’s MASE and CRPS on an 84-window fixture) | 🤗 Timestral-209M-CoreAI | TimestralGate (iPhone / Mac gate) | Modified MIT (Mistral AI; revenue condition) |
▸ Run in app — apps in apps/ live in this repo; ↗ links a
CoreAIKit example app; ‡ = app
wiring in progress. 🔧 = the bundle still carries coreai-torch 0.4.0-era IR, which every
OS 27 build from beta 2 on refuses at load (measured through macOS 26A428, 2026-09-15); it
is queued for the in-place strip_debug_info repair (see the
recovery note at the end).
Full app list: apps/README.md.
GA validation (the badge at the top) is staged, not assumed: on the day the release OS
lands, the most-downloaded bundles are downloaded and loaded through the runtime on that build
(python3 conversion/zoo_smoke.py --top 20), and the rest of the catalog follows over the next
week. The badge counts only bundles checked on the current release build; per-bundle status and
the build that checked it are in models/_INVENTORY.md.
A bundle that fails there is repaired or re-exported, not relabelled.
Decode throughput (tok/s, greedy; output top-1 exact vs the Hugging Face reference)
| iPhone 17 Pro · GPU | iPhone 17 Pro · ANE | M4 Max · GPU | |
|---|---|---|---|
| Qwen3.5-0.8B | 71.9 | 14.7 | 210 |
| Qwen3.5-2B | 29 | — | 161 |
| LFM2.5-1.2B | 45.4 | — | 276.5 |
| LFM2.5-2.6B (reasoning; int8hu / int4lin 2.0 GB) | not measured | — | 116.7 / 139.2 |
LFM3-1.7B (reasoning; Mac: int8, 64-token chunked prefill, the .aimodel on the kit’s path, macOS 27.0, first token 0.83 s from a 1,024-token prompt; iPhone: an iPhone 18 Pro Neural Engine bundle only — 8-bit palettized static, AOT h19p, a 768 + 256 turn in 6.71 s) |
— | 47.2 (iPhone 18 Pro, Neural Engine) | 169.5 |
| Granite 4.0-H 1B | 36.3 | — | 136.5 |
| Nanbeige4.1-3B | 15.9 | — | 114.5 |
| Nanbeige4.2-3B (22 physical / 44 executed+cache layers; int8) | pending | — | 46.4 |
| S1-mini (Superwhisper, ASR text normalizer, int8lin — device 276/276 + 27/27 token-exact; ⚠️ iOS cap: prompt+gen < 1024 tok) | 62.4 | — | 268.4 |
| MiniCPM5-1B (OpenBMB, int8 per-block-32 since 2026-09-09 — 24/24 + 6/6-incl.-stop on device, 16/16 on Mac; the earlier per-channel bundle never halted) | 61.7 | — | 246.6 |
| MiniCPM5-1B, Neural Engine (OpenBMB, 8-bit palettized static iOS export, AOT h18p — device gate PASS 3/3 vs fp32 HF, Apple’s 4-bit preset fails it; same-day interleaved A/B p128/g256 vs the int8 GPU row: 76.8 / 76.8 vs 67.1 / 64.4 at equal byte width) | 76.8 / 76.8 | — | — |
| MiniCPM5-2B (OpenBMB, int8 — 16/16 exact vs fp32 HF on Mac, 24/24 + 24/24 on device) | 22.4 | — | 127.6 |
| MiniCPM5-2B, Neural Engine (OpenBMB, 4-bit palettized static iOS export, AOT h18p — device gate PASS 3/3 vs fp32 HF; same-day interleaved A/B p128/g256 vs the int8 GPU row: 55.4 / 51.5 vs 22.8 / 20.6 — half the weight bytes, so not equal precision) | 55.4 / 51.5 | — | — |
| Youtu-LLM-2B (dense MLA, int8 — 16/16 device ≡ Mac ≡ HF) | ~19 (in-app ~24) | — | 102.8 |
| FastContext-1.0-4B (repo-exploration agent, 4bit — AOT h18p; ANE inference unsupported) | 20.4 | ✗ | — |
| BitCPM-8B (1.58-bit ternary, OpenBMB — custom 2-bit packed-GEMM kernel; AOT h18p; ~2.1 GB resident; token-exact 3/3 vs ref) | 17 | ✗ | 62.7 |
| Gemma 4 E2B | 30.3 (QAT 30.7) | 6 | 77.0 (QAT 78.9) |
| Gemma 4 E4B (official QAT) | 15.1 | — | 55.8 |
| Gemma 4 E2B VL (image+text, official QAT) | 25.5 | — | 82.4 |
| MiniCPM-V 4.6 (vision-language, sub-2B) | 53.4 | — | 224.3 |
| MiniCPM-V 4.7-1B (vision-language, int8hu; text-only trial p128 / g256; device 23/23 + 32/32 vs fp32 HF) | 98.0 (iPhone 18 Pro, PipelinedBench, device JIT) | — | 134.4 (Python runtime, one AOT h16c call at a time — not llm-benchmark) |
| LFM2.5-VL-450M (vision-language, int8lin — image buffer bound; vision encode 33.6 ms/image on iPhone, 18.0 on Mac) | 112.0 | — | 387.2 |
| LFM2.5-VL-3B (vision-language, int4 on iPhone / int8 on Mac; vision 75.7 ms/image) | 19.3–22.8 | — | 105.3 |
| North-Micro-Vision (vision-language 2.4B, int8lin — image bound; vision 83.4 ms/image on Mac) | 18.2 | — | 118.6 |
| Qwen3.6-35B-A3B (MoE, 35B/~3B active, Mac-only) | — | — | 64.9 † |
| Qwen3.6-27B (dense, Mac-only) | — | — | 15.9 |
| Qwen3.8-27B (dense VLM, Mac-only; vision 111 ms/image, VLM prefill 86.0 tok/s via pf16) | — | — | 15.7 |
| GLM-4.7-Flash (MoE + MLA, 30B/~3B active, Mac-only) | — | — | 52.4 † |
| Gemma 4 12B (dense, Mac-only) | — | — | 23 int8 / 33 int4 ‡ |
| Gemma 4 31B (dense, Mac-only) | — | — | 17.2 int4 ‡ |
Measured on the iOS 27 / macOS 27 developer betas (June–September 2026 builds; not yet re-measured on the release OS), Apple’s coreai-pipelined GPU engine, zero custom
kernels (ANE column + †/‡ excepted). † = MoE bundle using the custom
gather_qmm Metal kernel (reads only the routed
experts). ‡ = dense bundle whose full/global-attention SDPA is a custom flash-decode Metal
kernel — the stock MPSGraph SDPA crashed on the 12B’s 16-head × 512 Q on the June 2026 macOS 27 beta (a GPU scratch-heap overflow,
apple/coreai-models#27). On macOS 27.0 (26A428)
the 12B’s int8 export runs without the kernel, at the same 22.5 tok/s as the published int8 bundle
on that build (M4 Max, 2026-11-06; the table keeps the beta figure); a full 31B export without the
kernel has not been run there. Prefill, sizes, per-model caveats, and the Mac-only big models: models/.
Most downloaded
(auto-updated weekly from Hugging Face download counts)
Community ports
Ported by people outside this repo, published under their own Hugging Face namespace, and carrying their name on the card, on the row below, and in the release notes:
| Port | Contributor | PR |
|---|---|---|
| pocket-tts — Kyutai’s streaming TTS; the zoo’s first Kyutai model and first Mimi conversion, 7.8× real-time on iPhone 17 Pro Max at 169 MB | Rahul Rachuri | #12 |
| Parakeet-TDT-0.6B-v2 — the English-only sibling of the v3 port; 175.3× real-time over 66 minutes of audio on iPhone 17 Pro Max, 20376/20376 tokens exact | Rahul Rachuri | #13 |
| Bonsai 2 27B — PrismML’s ternary Qwen3.8-27B read straight from the PQ2_0 GGUF onto custom Metal kernels; 23.6 tok/s decode on an M4 Pro at ~7.5–8 GB, generations matching PrismML’s MLX runtime token for token on three prompts | Rahul Rachuri | #37 |
| Nanbeige4.2-3B — looped Llama, 22 physical blocks × 2 passes; the zoo’s first community port | Vadim Smirnov | #6 |
Thank you both — each of these arrived gated, and each one found a bug in this repo on the way
in. CONTRIBUTING.md is the path to the next row.
Recovery note — the coreai-torch 0.4.0 incident
Every model broken by the coreai-torch 0.4.0 debug-location issue has been re-published:
re-converted with 0.4.1 (verification gates re-run), or repaired in place with
strip_debug_info (debug locations removed,
weights byte-identical). Catalog-served apps just re-download. The few 🔧 rows above are the
remaining re-export queue. If you have your own 0.4.0-converted assets, strip_debug_info
fixes them in minutes — no re-conversion needed. (One port, FastContext-1.0-4B, was retired
instead of recovered: Microsoft removed its upstream weights on 2026-06-30, so it cannot be
rebuilt.) Details: knowledge/coreai-torch-041-ir-incident.md.