# Local model runtime & provisioning

The pipeline runs entirely on local models. This documents what works and the version traps, so the runtime
can be reproduced (reference environment: **x86_64 macOS, no GPU**; a Linux/GPU host is easier). Python venv:
`./.venv-local-asset/bin/python` (py3.12). Nothing here downloads at service runtime — models load offline.

## Models (each needs a provenance sidecar under `models/`)

| Model | Role | Runtime | Notes | License |
|---|---|---|---|---|
| Piper `en_US-lessac-medium` | voiceover | `piper-tts` 1.5.0 + onnxruntime | real speech; CLI takes `--model`/`--output_file` | MIT / Blizzard dataset |
| MusicGen small | music | `transformers` 4.44.2 | `facebook/musicgen-small`, CPU ~9× realtime, 32 kHz | **CC-BY-NC** |
| AudioLDM2 | SFX | `diffusers==0.30.3` | `cvssp/audioldm2`, CPU ~7× realtime, **16 kHz → resample** | **CC-BY-NC-SA** |
| moondream2 | Tier-2 vision director | `transformers` + `torchvision==0.17.2` | `vikhyatk/moondream2` rev `2024-08-26`, `trust_remote_code`, offline; ~25 s/frame CPU | Apache-2.0 |
| YOLOv8n (optional CV) | visual analysis | `.venv-local-cv` (ultralytics) | `./yolov8n.pt` loads offline; runs behind loopback HTTP `:8091` | **AGPL-3.0** |

## Version traps (x86_64 macOS — all real)

- **PyTorch caps at `torch==2.2.2` / `torchaudio==2.2.2`** (last x86_64 macOS wheels). numpy must be **< 2**
  (pinned `numpy==1.26.4`, `numba==0.60.0`, `llvmlite==0.43.0`, `scipy==1.13.1`).
- **transformers must be 4.x** (pinned `4.44.2`). transformers 5.x silently disables PyTorch (needs torch ≥ 2.4)
  → models unavailable.
- **audiocraft does NOT work here** (hard top-level `from xformers import ops`; xformers has no cp312 x86_64
  wheel/sdist). AudioGen is audiocraft-only → **SFX uses AudioLDM2 (diffusers)** instead. MusicGen runs via
  `transformers`, not audiocraft.
- **HF downloads:** set `HF_HUB_DISABLE_XET=1` (the xet CDN times out on some networks). HF may **429** after
  many pulls — retry resumes from cache.

## Wiring notes

- `tools/local_asset_worker.py` synthesizes voice/music/SFX; `tools/vision_caption.py` runs moondream (batch,
  load-once) for the Tier-2 director.
- `LocalAssetSynthesizer` requires each model path to be a licensed **regular file** (adjacent `.license.txt`,
  non-blank, not `UNTRACKED`) — `AssetLicensePolicy`.
- Do **not** run `tools/run_local_cv_worker.sh` / `tools/run_local_asset_worker.sh` in a certified environment:
  their `auto` modes `pip install` and can fetch a named YOLO model.
- **Licensing blocker for commercial use:** MusicGen (CC-BY-NC), AudioLDM2 (CC-BY-NC-SA) and YOLOv8 (AGPL) are
  non-commercial/copyleft. Swap in commercially-licensed models/assets before any commercial release. moondream2
  (Apache-2.0) and Piper are fine.

## Optional stronger Tier-2 VLM: Qwen2.5-VL via llama.cpp (recommended upgrade)

moondream2 is small and, on hard footage (distant, portrait), cannot reliably tell a highlight from an
aftermath — which caps the director's vision **judge** (see `docs/cinematic-quality-rules.md` R15). A stronger
local VLM is a **drop-in**: the director calls a captioner script with a fixed manifest→JSON contract, so only
the configured script path changes. **`tools/vision_caption_llamacpp.py`** implements that contract against
**llama.cpp** (GGUF) — which runs on this **x86 CPU via AVX SIMD** and **bypasses the torch==2.2.2 /
transformers 4.x / no-xformers trap entirely** (no PyTorch involved).

**Recommended model:** `Qwen2.5-VL-3B-Instruct` (Apache-2.0 — commercial-friendly; verify the model card),
`Q4_K_M` GGUF + its `mmproj` vision projector. `Qwen3-VL-2B/4B` (official GGUF) or `Gemma 3 4B` are alternatives.

Provision **offline** (on a networked machine, then copy the files over — nothing downloads at service runtime).
The recipe below is the **verified** one for this reference machine (x86_64 macOS, Command Line Tools, no usable
GPU):

```bash
# 1) Build llama.cpp with the multimodal CLI (one-time; needs cmake + a C++ compiler; NOT in a certified env).
#    GOTCHA (Command Line Tools, not full Xcode): clang can't find libc++ headers (<array> not found) because
#    they live only under the SDK. Point CMAKE at them, or the ggml-base compile fails:
CXXV1=$(xcrun --show-sdk-path)/usr/include/c++/v1
git clone --depth 1 https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_NATIVE=ON -DLLAMA_CURL=OFF \
      -DCMAKE_CXX_FLAGS="-isystem $CXXV1" -DCMAKE_C_FLAGS="-isystem $CXXV1"
cmake --build build --config Release -j --target llama-mtmd-cli   # -> build/bin/llama-mtmd-cli

# 2) Fetch the GGUF weights + mmproj (verified: unsloth/Qwen2.5-VL-3B-Instruct-GGUF)
#    Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf (~1.9GB)  and  mmproj-F16.gguf (~1.3GB)
#    Record license/SHA-256 next to each file, per the asset-provenance policy.
```

Enable it (no code change — config + env only):

```yaml
# application-localpoc.yml  (video-clipping.editing)
vision-caption-script: ./tools/vision_caption_llamacpp.py
```
```bash
export LLAMACPP_MTMD_BIN=/abs/llama.cpp/build/bin/llama-mtmd-cli
export LLAMACPP_VLM_MODEL=/abs/models/qwen2.5-vl-3b/Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf
export LLAMACPP_VLM_MMPROJ=/abs/models/qwen2.5-vl-3b/mmproj-F16.gguf
# LLAMACPP_VLM_NTOKENS=64 (optional)
```

**Runtime GOTCHA — force CPU on an Intel Mac.** The build enables Metal by default, but this machine's
integrated GPU times out on the vision encoder (`ggml_metal_synchronize: command buffer failed … GPU Timeout`).
`tools/vision_caption_llamacpp.py` therefore always passes `-ngl 0 --no-mmproj-offload` (pure CPU/AVX). Expect
~1–3 min per frame on CPU; ~7 frames per clip (candidates + overlay) ≈ 10–20 min, fine for offline batch.
A persistent `llama-server` backend would avoid per-frame reloads — a future optimisation.

**Verified:** Qwen2.5-VL-3B **does** break the moondream ceiling. On the bowling celebration frame moondream
said "standing in a bowling alley"; Qwen2.5-VL says *"The person is raising their arms in a celebratory
gesture"* (highlight-worthiness 1.0), and correctly rates the turn-around/anticipation frames low (0.2 / 0.15)
— so the director's vision judge picks the celebration. This is the recommended production Tier-2 model.