| import json |
| import io |
|
|
| path = r'c:\Users\vaibhav patil\.gemini\antigravity\scratch\financial-doc-extractor\scripts\financial-doc-training (1).ipynb' |
| with open(path, 'r', encoding='utf-8') as f: |
| nb = json.load(f) |
|
|
| modifications = 0 |
| for cell in nb.get('cells', []): |
| if cell['cell_type'] == 'code': |
| source = cell['source'] |
| if isinstance(source, list): |
| source = ''.join(source) |
| |
| if 'import os' in source and 'import torch' in source and 'CUDA_VISIBLE_DEVICES' not in source: |
| source = source.replace('import os\n', 'import os\nos.environ["CUDA_VISIBLE_DEVICES"] = "0"\n') |
| modifications += 1 |
| |
| if 'target_modules=["q_proj", "v_proj"],' in source: |
| source = source.replace('target_modules=["q_proj", "v_proj"],', 'target_modules="all-linear",') |
| source = source.replace('# HARDCODING to 2 layers! Cuts activation memory by 60%', '# FIXED: "all-linear" enables Unsloth memory efficient fused Triton kernels.') |
| modifications += 1 |
| |
| if 'TrainingArguments(' in source and 'fp16' not in source: |
| source = source.replace('report_to="none",', 'report_to="none",\n fp16=not torch.cuda.is_bf16_supported(),\n bf16=torch.cuda.is_bf16_supported(),') |
| modifications += 1 |
| |
| if isinstance(cell['source'], list): |
| |
| cell['source'] = [s + '\n' for s in source.split('\n')] |
| if cell['source'] and cell['source'][-1].endswith('\n') and not source.endswith('\n'): |
| cell['source'][-1] = cell['source'][-1][:-1] |
| else: |
| cell['source'] = source |
|
|
| if modifications > 0: |
| with open(path, 'w', encoding='utf-8') as f: |
| json.dump(nb, f) |
| print(f'Successfully applied {modifications} fixes to the notebook.') |
| else: |
| print('No changes needed or could not find targets.') |
|
|
