v0.0.5

Other

• streaming context 2/2 by default, faster SNAC, README section
• Orpheus text to speech: Llama 3 on the native engine, SNAC decoder, oxinfer::tts
• README section, model table rows, tools/ref_janus.py
• text to image and image understanding
• oxideav-core types in the API; oxideav feature; media without oxideav-meta
• Typed decisions over images (JEV-27B-VL, JEV-9B vl/)
• image, video and audio input (Qwen and Gemma 4), agreement and speed
• Vision towers: torchvision-exact resize, GPU matmuls, bf16 Qwen tower
• Qwen3.5/3.6/3.8 image and video input, Gemma 4 video input
• Whisper speed table (final measurements, 6-thread rows)
• --threads
• bf16 cross-attention keys and values below f16 decoder precision
• Whisper agreement for base, medium, large-v3 and tiny.en
• share the engine's run lock, en for English-only models, rule and segment tests
• Whisper speech to text (models, usage, agreement with transformers, speed); tools/ref_whisper.py
• fused attention kernel for 64-wide heads (Whisper encoder, SD 2.x)
• Whisper speech to text: transformers-exact features, encoder/decoder, long-form decoding, timestamps
• project goals (run everything everywhere, then make it fast)
• JEV-35B (merged, LM-head readout) measurements
• typed decisions (Jev models), measured agreement and latency
• Jev typed decisions: runtime LoRA, Engine::decide, C API oxi_decide, CLI --decide
• Core ML converter: block-shifting Gemma local cache, prefill and infer bit-identical
• Core ML runtime: shifting local caches (local_cache_length) are fed exactly once, never padded
• Core ML converter: Fisher-weighted LUTs, mixed precision, shifting Gemma local cache, Qwen3.5 MoE
• llama.cpp comparison rerun for oxinfer after the matrix-unit and multi-token kernel merges
• interleaved oxinfer vs llama.cpp benchmark; README comparison tables
• SD 1.5 with the LCM-LoRA as the default image model, LoRA files in the browser build