Any-to-Any
Transformers
Safetensors
neo_chat
image-feature-extraction
multimodal
text-to-image
image-to-text
image-editing
interleaved-generation
custom_code
Instructions to use sensenova/SenseNova-U1-8B-MoT-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sensenova/SenseNova-U1-8B-MoT-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sensenova/SenseNova-U1-8B-MoT-SFT", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 911 Bytes
166efa1 | 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 | #!/usr/bin/env python3
"""Upload this directory to Hugging Face (excludes .git). Run from repo root or pass --dir."""
import argparse
import os
from huggingface_hub import HfApi, login
def main():
p = argparse.ArgumentParser()
p.add_argument(
"--dir",
default=os.path.dirname(os.path.abspath(__file__)),
help="Folder to upload (default: this script's directory)",
)
p.add_argument(
"--repo-id",
default="sensenova/SenseNova-U1-Mini-SFT",
)
args = p.parse_args()
token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGING_FACE_HUB_TOKEN")
if token:
login(token=token)
else:
login()
HfApi().upload_folder(
folder_path=args.dir,
repo_id=args.repo_id,
repo_type="model",
ignore_patterns=[".git/**"],
)
print("Done:", args.repo_id)
if __name__ == "__main__":
main() |