Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/matilda-jev-fp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-fp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-fp4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/matilda-jev-fp4") model = AutoModel.from_pretrained("Maincode/matilda-jev-fp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download kev/continuation.py from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 2.42 kB
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/continuation.py
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/kev/continuation.py
-
curl -L -o continuation.py https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/continuation.py
2.42 kB
| """Guard an explicitly requested extension while preserving ordinary exact resume checks.""" | |
| import hashlib | |
| import math | |
| from pathlib import Path | |
| from kev.train import RESUME_INVARIANT | |
| def validate_extension(meta: dict, config: dict, total_steps: int, hashes: dict, source_run: Path) -> int: | |
| saved = meta['config'] | |
| start = int(meta['step']) | |
| if start != meta['total_steps'] or start < 1: | |
| raise ValueError('Only a completed source run may be extended') | |
| if config['epochs'] != saved['epochs'] + 1: | |
| raise ValueError('This extension must add exactly one epoch') | |
| steps_per_epoch = math.ceil(config['train_rows'] / config['effective_batch_size']) | |
| if start != steps_per_epoch * saved['epochs'] or total_steps != start + steps_per_epoch: | |
| raise ValueError('Unexpected epoch or batch schedule') | |
| if meta['examples_seen'] != config['train_rows'] * saved['epochs']: | |
| raise ValueError('Source run did not consume all examples') | |
| if meta['data_sha256'] != hashes or saved['data_sha256'] != hashes: | |
| raise ValueError('Extension data differs from the saved run') | |
| if not 0 < config['lr'] < saved['lr']: | |
| raise ValueError('Extension peak learning rate must be positive and lower') | |
| if not 0 <= config['warmup_fraction'] < 1 or not 0 <= config['min_lr_ratio'] <= 1: | |
| raise ValueError('Invalid extension learning-rate schedule') | |
| if config['world_size'] != saved['world_size']: | |
| raise ValueError('Extension must preserve the FSDP world size') | |
| allowed = {'epochs', 'lr', 'warmup_fraction', 'min_lr_ratio', 'extend_from'} | |
| for key in RESUME_INVARIANT: | |
| if key not in allowed and config[key] != saved[key]: | |
| raise ValueError(f'Extension changes an unrelated setting: {key}') | |
| source_package = source_run.resolve().parents[1] / 'kev/src/kev' | |
| for name, expected in saved['code_sha256'].items(): | |
| path = source_package.parents[1] / name if name == 'uv.lock' else source_package / name | |
| actual = hashlib.sha256(path.read_bytes()).hexdigest() | |
| if actual != expected: | |
| raise ValueError(f'Source implementation changed: {name}') | |
| for name in ['model.py', 'evaluate.py', 'types.py', 'uv.lock']: | |
| if config['code_sha256'].get(name) != saved['code_sha256'].get(name): | |
| raise ValueError(f'Extension changes model or evaluation implementation: {name}') | |
| return start | |