| """Inspect a LoRA .safetensors file: embedded training metadata + inferred |
| rank/target modules from tensor shapes. Read-only, no GPU/torch needed -- |
| just parses the safetensors header, so it's fast and safe to run on any |
| LoRA file (including ones you didn't train yourself). |
| |
| Usage: |
| python_embeded\\python.exe dataset_builder\\inspect_lora.py path\\to\\lora.safetensors |
| """ |
| import argparse |
| import json |
| import re |
| from collections import Counter |
|
|
| from safetensors import safe_open |
|
|
|
|
| def parse_args(): |
| p = argparse.ArgumentParser(description="Inspect a LoRA safetensors file's metadata") |
| p.add_argument("lora_path") |
| return p.parse_args() |
|
|
|
|
| def infer_rank(keys, shapes): |
| for k in keys: |
| if k.endswith("lora_A.weight") or k.endswith("lora_down.weight"): |
| return shapes[k][0] |
| return None |
|
|
|
|
| def summarize_target_modules(keys): |
| """Collapses per-block keys (blocks.0, blocks.1, ...) into module families |
| so you can see what parts of the model were trained without a 800-line dump.""" |
| families = Counter() |
| for k in keys: |
| base = re.sub(r"\.(lora_A|lora_B|lora_down|lora_up|alpha)(\.weight)?$", "", k) |
| base = re.sub(r"\.\d+\.", ".N.", base) |
| families[base] += 1 |
| return families |
|
|
|
|
| def main(): |
| args = parse_args() |
| with safe_open(args.lora_path, framework="pt") as f: |
| meta = f.metadata() or {} |
| keys = list(f.keys()) |
| shapes = {k: f.get_slice(k).get_shape() for k in keys} |
|
|
| print(f"=== {args.lora_path} ===") |
| print(f"Tensor count: {len(keys)}") |
| print(f"Inferred rank (lora_A/lora_down first dim): {infer_rank(keys, shapes)}") |
|
|
| print("\n--- Embedded metadata ---") |
| if not meta: |
| print("(none)") |
| for k, v in meta.items(): |
| try: |
| parsed = json.loads(v) |
| print(f"{k}:") |
| print(json.dumps(parsed, indent=2, ensure_ascii=False)[:3000]) |
| except (json.JSONDecodeError, TypeError): |
| print(f"{k}: {v}") |
|
|
| print("\n--- Target module families (per-block keys collapsed) ---") |
| for base, count in summarize_target_modules(keys).most_common(30): |
| print(f" {base} x{count}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
