Instructions to use ixim/Image21-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ixim/Image21-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download ixim/Image21-MLX-8bit --local-dir Image21-MLX-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,255 Bytes
4f03424 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | """Verify the complete BF16 source against the saved official revision."""
import argparse
import json
import platform
import subprocess
from pathlib import Path
from scripts.common import SOURCE, REVISION, inventory, sha256, write_json
def main():
ap = argparse.ArgumentParser()
ap.add_argument('--source', type=Path, default=SOURCE)
ap.add_argument('--output', type=Path, default=Path('artifacts/source-audit.json'))
ap.add_argument('--manifest', type=Path, help='Published evaluation/source-files.json for a portable rebuild')
args = ap.parse_args()
if args.manifest:
info=json.loads(args.manifest.read_text())
if info['revision']!=REVISION: raise ValueError('Unexpected source revision')
rows=info['files']
else:
info = json.loads(Path('artifacts/upstream/source-model-info.json').read_text())
if info['sha'] != REVISION:
raise ValueError('Unexpected source revision')
rows = [{'path': x['rfilename'], 'size': x['size'], 'sha256': x['lfs']['sha256']}
for x in info['siblings'] if x['rfilename'].endswith('.safetensors')]
rows += [r for r in json.loads(Path('artifacts/upstream/source-config-manifest.json').read_text())
if r['path'] not in ('.gitattributes', 'README.md')]
for row in rows:
file = args.source / row['path']
if file.stat().st_size != row['size'] or sha256(file) != row['sha256']:
raise ValueError(f'Source mismatch: {file}')
print('Verified', row['path'], flush=True)
inv = inventory(args.source)
estimates = {}
for bits in (4, 6, 8):
estimates[str(bits)] = sum(v['tensor_bytes'] - v['quantizable_parameters'] * 2 +
v['quantizable_parameters'] * (bits / 8 + 4 / 64)
for v in inv.values())
write_json(args.output, dict(source=str(args.source), revision=REVISION, files=rows,
inventory=inv, estimated_weight_bytes=estimates,
platform=platform.platform()))
print(json.dumps(dict(inventory=inv, estimated_weight_GiB={k:v/2**30 for k,v in estimates.items()}),indent=2))
if __name__ == '__main__':
main()
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