--- license: apache-2.0 base_model: OpenMOSS-Team/MOSS-Audio-4B-Instruct tags: - audio - audio-text-to-text - maestro --- # MOSS-Audio 4B — MAESTRO mirror Verbatim mirror of [OpenMOSS-Team/MOSS-Audio-4B-Instruct](https://huggingface.co/OpenMOSS-Team/MOSS-Audio-4B-Instruct) (revision `6907a499dc0e87cc77c8ae0fe23fd0eb5476a02d`, 2026-04) packaged for the MAESTRO DAW's offline runtime. MOSS-Audio 4B-Instruct (OpenMOSS) is a general audio-understanding LLM — a Whisper-style audio encoder feeding a Qwen3-4B language model with DeepStack feature injection. It captions any recording (speech, sounds, music), transcribes speech with timestamps, reports on speakers and their emotional state, lists sound events, and answers free-form questions about the audio. ## Layout ``` bf16/ everything MAESTRO downloads (manifest allow_patterns) model-0000{1..3}-of-00003.safetensors bf16 shards, byte-identical to upstream model.safetensors.index.json config.json generation_config.json tokenizer_config.json vocab.json merges.txt added_tokens.json special_tokens_map.json processor_config.json chat_template.jinja sha256.json per-file hashes of this folder README.md this file ``` ## Differences from upstream - The remote-code Python files (`configuration_moss_audio.py`, `processing_moss_audio.py`) are **not** shipped: MAESTRO vendors the upstream GitHub `src/` inference package (github.com/OpenMOSS/MOSS-Audio @ `ce783cb7`) and never uses `trust_remote_code`. (Upstream's weights repo ships no modeling file at all — the GitHub package is the actual torch implementation.) - The `assets/` figures and upstream README are not shipped. - Weight shards are byte-identical to upstream (sha256-verified against the upstream LFS metadata at mirror time — see `bf16/sha256.json`). ## License Apache-2.0, same as upstream code and weights. Copyright the OpenMOSS team. This mirror exists so MAESTRO installs pull a pinned, verified snapshot; all credit for the model goes to its authors.