Sachin Desai

Core AI (.aimodel) conversion of NVIDIA's streaming speaker-diarization model (up to eight speakers, 10 ms output frames) for iOS 27+ / macOS 27+. Converted from checkpoint revision (Nemotron-3-Diarization.nemo, SHA-256 867c53f552998f772e5b5e5c082962ae85ee7ca5669c2bc17d7f615133d4e96d) with coreai-torch 0.4.1 (standard graphs) and 0.4.2 (Neural Engine encoder), coreai-core 1.0.0b2. No weights were retrained, quantized or pruned. The model is split into two graphs that a host streaming loop calls once per encoder/head (packed speaker cache + FIFO + chunk embeddings → speaker probabilities). The Arrival-Order Speaker Cache, FIFO and chunking logic of Streaming Sortformer run on the host; see…

Core ML conversion of NVIDIA's streaming speaker-diarization model (up to eight speakers, 10 ms output frames) for iOS 18+ / macOS 15+. Converted from checkpoint revision (Nemotron-3-Diarization.nemo, SHA-256 No weights were retrained, quantized or pruned. The model is split into two graphs that a host streaming loop calls once per encoder/head (packed speaker cache + FIFO + chunk embeddings → speaker probabilities). The Arrival-Order Speaker Cache, FIFO and chunking logic of Streaming Sortformer run on the host and are not part of these graphs; see "Streaming loop" below..f32le files are raw little-endian float32 in C order. The.mlpackage directories load with MLModel.compileModel(at:) /…

Core AI port of fastino/gliner2.5-multi-v1, the multilingual GLiNER2.5 boundary extractor. It is built on mDeBERTa-v3-base and has 287M parameters. It keeps the upstream decision behavior. All credit for the model goes to Fastino and the GLiNER2 authors. The original model card is included as - Checkpoint fastino/gliner2.5-multi-v1 at revision 12fc40399dae672ce840c5e3c50a92340bff3c8c. 55656fb (for preprocessing and decoding). - coreai-torch 0.4.3, coreai-core 1.0.0b3, Torch 2.11.0. - Learned weights are unchanged (FP16). The export only rewrites graph structure: - Constant DeBERTa relative-position buckets. The float log-bucket path produces wrong indices inside converted graphs. - A…

LiquidAI/d1-omni-600M converted to Apple Core AI, re-authored to run on the Neural Engine, with GPU models for a Mac and as an iPhone fallback for long inputs. d1-omni-600M is a 587M-parameter decision model: give it a state — text, JSON, a photo or a voice clip — and named questions, and it returns typed answers (a yes/no probability, a pick among named options, or a level) read from its own distribution in one pass, with no generated tokens. These are the portable models behind the D1Omni iPhone app (camera, voice and text decisions on device). Compile them for your device with compile.sh (one command, ~2.5 min). compile.sh runs xcrun coreai-build compile (Xcode with Core AI) on the three…

An MLX 4-bit conversion of NVIDIA's for Apple silicon. This is a community conversion. It is not made, reviewed, or endorsed by NVIDIA. For the model's design, training data, benchmarks, and intended use, see Dory 1.0 4B is a looped (latent-recurrent) normalized-GPT model trained by NVIDIA. Its layers form three blocks: input, recurrent, and output. The shared recurrent block runs several times per token, and the input block's output is added back at the start of each loop. More loops give more compute per token and better accuracy from the same weights. It mixes 1,024-token sliding-window attention with full attention, supports up to 256K tokens of context, and reasons in a block before it…