| """Explicit adapter overlays; the downloaded package is never overwritten.""" |
| import hashlib |
| import json |
| import math |
| from pathlib import Path |
|
|
| from .integrity import file_sha256 |
|
|
|
|
| def base_identity(root): |
| manifest = json.loads((Path(root) / 'manifest.json').read_text()) |
| weights = {k: v for k, v in manifest['files'].items() |
| if k.startswith(('checkpoint/', 'adapter/'))} |
| return hashlib.sha256(json.dumps(weights, sort_keys=True).encode()).hexdigest() |
|
|
|
|
| def verify_adaptation(root, directory): |
| directory = Path(directory).resolve() |
| meta = json.loads((directory / 'adaptation.json').read_text()) |
| if meta.get('format') != 'fev-adaptation-v1' or meta.get('baseIdentity') != base_identity(root): |
| raise ValueError('Adaptation does not match the packaged base and original adapter') |
| if set(meta.get('files', {})) != {'adapter_config.json', 'adapter_model.safetensors'}: |
| raise ValueError('Unexpected adaptation payload') |
| for name, digest in meta['files'].items(): |
| path = directory / name |
| if path.is_symlink() or file_sha256(path) != digest: |
| raise ValueError('Adaptation checksum mismatch') |
| if json.loads((directory / 'adapter_config.json').read_text()) != json.loads((Path(root) / 'adapter/adapter_config.json').read_text()): |
| raise ValueError('Adapter architecture differs from the released checkpoint') |
| temperature = meta.get('temperature') |
| if type(temperature) not in (int, float) or not math.isfinite(temperature) or temperature <= 0: |
| raise ValueError('Invalid adaptation temperature') |
| return meta |
|
|
|
|
| def load_adaptation(wrapper, directory): |
| from peft import get_peft_model_state_dict, set_peft_model_state_dict |
| from safetensors.torch import load_file |
| meta = verify_adaptation(wrapper.root, directory) |
| state = load_file(str(Path(directory) / 'adapter_model.safetensors')) |
| text = wrapper.model.backbone.language_model |
| existing = get_peft_model_state_dict(text) |
| if set(state) != set(existing) or any(state[k].shape != existing[k].shape for k in state): |
| raise ValueError('Adaptation tensor keys or shapes differ') |
| if any(v.dtype != existing[k].dtype or not v.isfinite().all() for k, v in state.items()): |
| raise ValueError('Adaptation tensor dtype or finite-value check failed') |
| restored = set_peft_model_state_dict(text, state) |
| if restored.unexpected_keys or any('lora_' in k for k in restored.missing_keys): |
| raise ValueError('Adaptation was not restored completely') |
| wrapper.model.temperature = meta['temperature'] |
| return meta |
|
|
