| import torch |
| ckpt = torch.load('ckpts/step_259567/archon.pt', map_location='cpu', weights_only=False) |
| print(f'step: {ckpt["step"]}') |
| print(f'wat_buffer_size: {ckpt["wat_buffer_size"]}') |
| print(f'optimizer keys: {list(ckpt["optimizer"].keys())}') |
| m = ckpt['model'] |
| print(f'\nmodel state_dict first 15 keys + dtypes:') |
| for k in list(m.keys())[:15]: |
| v = m[k] |
| shape = tuple(v.shape) if hasattr(v, 'shape') else type(v).__name__ |
| dt = v.dtype if hasattr(v, 'dtype') else '' |
| print(f' {k:50s} {str(shape):25s} {dt}') |
| print(f'\nmodel state_dict last 10 keys:') |
| for k in list(m.keys())[-10:]: |
| v = m[k] |
| shape = tuple(v.shape) if hasattr(v, 'shape') else type(v).__name__ |
| dt = v.dtype if hasattr(v, 'dtype') else '' |
| print(f' {k:50s} {str(shape):25s} {dt}') |
| total = sum(v.numel() for v in m.values() if hasattr(v, 'numel')) |
| print(f'\nTotal params: {total/1e6:.1f}M') |
| print(f'\n--- config.py ---') |
| with open('source/config.py') as f: |
| print(f.read()) |
|
|