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File size: 3,684 Bytes
3865888 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | """Local model registry (V6 phase_02): every entry is measured, never claimed.
Tasks: TEXT_MODEL | VISION_MODEL | IMAGE_MODEL | STT_MODEL | TTS_MODEL |
EMBEDDING_MODEL. A model is VERIFIED only after load + inference/encode
evidence exists (see diagnostics/v6/*_verification.json).
"""
import hashlib
import json
import os
from typing import Any, Dict, List, Optional
REGISTRY_PATH = os.path.join("models", "registry.json")
# Default acquisition candidates (pinned revisions where known).
DEFAULTS: List[Dict[str, Any]] = [
{"id": "Qwen/Qwen3-0.6B-GGUF", "task": "TEXT_MODEL",
"file": "Qwen3-0.6B-Q8_0.gguf", "revision": "main",
"license": "Apache-2.0", "backend": "llama_cpp",
"precision": "Q8_0", "memory_estimate_mb": 700},
{"id": "sentence-transformers/all-MiniLM-L6-v2", "task": "EMBEDDING_MODEL",
"revision": "main", "license": "Apache-2.0", "backend": "sentence_transformers",
"precision": "fp32", "memory_estimate_mb": 120},
{"id": "openai/whisper-small", "task": "STT_MODEL",
"revision": "main", "license": "Apache-2.0", "backend": "transformers",
"precision": "fp32", "memory_estimate_mb": 1000},
{"id": "hexgrad/Kokoro-82M", "task": "TTS_MODEL",
"revision": "main", "license": "Apache-2.0", "backend": "kokoro",
"precision": "fp32", "memory_estimate_mb": 400},
{"id": "Lykon/dreamshaper-8-lcm", "task": "IMAGE_MODEL",
"revision": "main", "license": "CreativeML Open RAIL-M", "backend": "diffusers",
"precision": "fp16-cpu-fp32", "memory_estimate_mb": 4500},
]
def _sha256(path: str) -> Optional[str]:
if not os.path.exists(path):
return None
h = hashlib.sha256()
with open(path, "rb") as f:
for chunk in iter(lambda: f.read(65536), b""):
h.update(chunk)
return h.hexdigest()
def task_dir(task: str) -> str:
return os.path.join("models", task.lower())
def scan_local() -> List[Dict[str, Any]]:
"""Inspect models/ and report per-file measured facts (size, sha)."""
entries = []
for cand in DEFAULTS:
d = task_dir(cand["task"])
local = None
if os.path.isdir(d):
cands = []
for fn in sorted(os.listdir(d)):
p = os.path.join(d, fn)
if not os.path.isfile(p):
continue
if cand.get("file") and fn != cand["file"]:
continue
if fn.split(".")[-1] not in ("gguf", "safetensors", "bin", "onnx", "pt"):
continue
cands.append((p, os.path.getsize(p)))
if cands:
# prefer safetensors, then largest weight file
cands.sort(key=lambda t: (0 if t[0].endswith(".safetensors") else 1,
-t[1]))
p, size = cands[0]
local = {"path": p, "size": size}
entry = dict(cand)
entry["local_path"] = local["path"] if local else None
entry["file_size"] = local["size"] if local else None
entry["sha256"] = _sha256(local["path"]) if local else None
entry["present"] = local is not None
entry["verified"] = False # set True only by verification scripts
entries.append(entry)
return entries
def write_registry(verified: Optional[Dict[str, bool]] = None) -> List[Dict[str, Any]]:
entries = scan_local()
if verified:
for e in entries:
if e["id"] in verified:
e["verified"] = bool(verified[e["id"]])
os.makedirs(os.path.dirname(REGISTRY_PATH), exist_ok=True)
json.dump(entries, open(REGISTRY_PATH, "w", encoding="utf-8"), indent=2)
return entries
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