Feature Extraction
Transformers
Safetensors
sdar
llama-factory
full
Generated from Trainer
custom_code
Instructions to use autoprogrammer/sdar_4b_multi_block_causal-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_causal-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_causal-final", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("autoprogrammer/sdar_4b_multi_block_causal-final", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fd07ec43eeaad38aa021786f268c12dd1c5ec36f348605b1024f39d81130f3c
- Size of remote file:
- 7.89 kB
- SHA256:
- b94e3aa7c8c3122b3bba4ab7efe4a5b72b6efbec734ab3fb35ed2938c134e4b8
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