Feature Extraction
Transformers
Safetensors
sdar
llama-factory
full
Generated from Trainer
custom_code
Instructions to use autoprogrammer/sdar_4b_multi_block-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoprogrammer/sdar_4b_multi_block-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="autoprogrammer/sdar_4b_multi_block-final", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("autoprogrammer/sdar_4b_multi_block-final", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2e23122c4c3afe0f733d8b01c5b74e836c637b3539d2179450874956fd7e2cf3
- Size of remote file:
- 7.89 kB
- SHA256:
- d3080b15407779d5e6914aff558133f27daaa1f8d4de0d0c3e65ce0727420dbf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.