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publish_ai_models.py produces the CDN artifacts for every AI_MODELS entry — GPG-signed .meta4 sidecars (reusing the map trust chain) and the signed ai/models.json manifest — and uploads them with s3cmd. The bundled catalog now carries the real sha256/size of the two Qwen GGUFs, which match Hugging Face's LFS records for the Apache 2.0 unsloth quants, and whose signed meta4s verify against the committed public key. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
108 lines
4 KiB
Python
108 lines
4 KiB
Python
"""The AI model catalog.
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The bundled AI_MODELS constant is the offline fallback (like the map module's MAP_REGIONS);
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the CDN manifest at ai/models.json is fetched at browse/download time and GPG-verified with the
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committed WROLPi public key. The release process refreshes AI_MODELS from the published manifest.
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"""
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import json
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import tempfile
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from pathlib import Path
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import psutil
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from wrolpi.common import aiohttp_get, get_media_directory, logger, verify_gpg_signature
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logger = logger.getChild(__name__)
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CDN = 'https://wrolpi.nyc3.cdn.digitaloceanspaces.com'
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MODELS_MANIFEST_URL = f'{CDN}/ai/models.json'
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# Sizes are bytes; sha256 values match the published manifest (wrolpi/scripts/publish_ai_models.py
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# refreshes both). The meta4 sidecar (GPG-verified at download time) is what enforces the hash.
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AI_MODELS = [
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dict(
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name='Qwen3-1.7B-Q4_K_M.gguf',
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tier='small',
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url=f'{CDN}/ai/Qwen3-1.7B-Q4_K_M.gguf',
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sha256='b139949c5bd74937ad8ed8c8cf3d9ffb1e99c866c823204dc42c0d91fa181897',
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size=1_107_409_472,
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min_ram_gb=4,
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default_context=8_192,
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license='Apache 2.0',
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description='The default assistant for 4GB devices (Pi 4/5). Best tool-calling per byte.',
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),
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dict(
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name='Qwen3-4B-Instruct-2507-Q4_K_M.gguf',
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tier='medium',
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url=f'{CDN}/ai/Qwen3-4B-Instruct-2507-Q4_K_M.gguf',
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sha256='3605803b982cb64aead44f6c1b2ae36e3acdb41d8e46c8a94c6533bc4c67e597',
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size=2_497_281_120,
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min_ram_gb=8,
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default_context=16_384,
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license='Apache 2.0',
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description='The default assistant for 8GB+ devices. A standout small tool-caller.',
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),
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]
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def get_models_directory() -> Path:
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"""Where GGUF model files are stored. Always inside the media directory."""
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return get_media_directory() / 'ai/models'
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async def fetch_models_manifest(url: str = None) -> dict:
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"""Fetch the models manifest from the CDN and verify its GPG signature (map-manifest pattern)."""
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url = url or MODELS_MANIFEST_URL
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sig_url = f'{url}.sig'
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async with aiohttp_get(url, timeout=30) as response:
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if response.status != 200:
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raise RuntimeError(f'Failed to fetch models manifest: HTTP {response.status}')
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manifest_bytes = await response.content.read()
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async with aiohttp_get(sig_url, timeout=30) as response:
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if response.status != 200:
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raise RuntimeError(f'Failed to fetch models manifest signature: HTTP {response.status}')
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signature_bytes = await response.content.read()
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with tempfile.TemporaryDirectory() as tmpdir:
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manifest_path = Path(tmpdir) / 'models.json'
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sig_path = Path(tmpdir) / 'models.json.sig'
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manifest_path.write_bytes(manifest_bytes)
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sig_path.write_bytes(signature_bytes)
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if not await verify_gpg_signature(manifest_path, sig_path):
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raise RuntimeError('Models manifest GPG signature verification failed')
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logger.info('Models manifest GPG signature verified successfully')
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return json.loads(manifest_bytes)
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async def get_models_catalog() -> (list, str):
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"""The list of downloadable models, and where it came from ('cdn' or 'bundled').
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The CDN manifest wins when reachable; the bundled constant keeps the Manage tab working
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offline."""
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try:
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manifest = await fetch_models_manifest()
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models = manifest.get('models') or []
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if models:
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return models, 'cdn'
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except Exception as e:
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logger.debug(f'Could not fetch models manifest, using bundled catalog: {e}')
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return AI_MODELS, 'bundled'
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def get_total_ram_bytes() -> int:
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return psutil.virtual_memory().total
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def recommend_tier(total_ram_bytes: int = None) -> str:
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"""Recommend a model tier for this device's RAM. Other tiers stay selectable with a warning."""
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total_ram_bytes = total_ram_bytes if total_ram_bytes is not None else get_total_ram_bytes()
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gb = total_ram_bytes / 1024 ** 3
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if gb < 6:
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return 'small'
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if gb < 24:
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return 'medium'
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return 'large'
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