Instructions to use chaimachabir/lora-data1-data2-data3-data4-data5-tinyllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use chaimachabir/lora-data1-data2-data3-data4-data5-tinyllama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "chaimachabir/lora-data1-data2-data3-data4-data5-tinyllama") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f251dc100fcd08c8a28c9d26bbbbb66e4fcdfe4c7d61bea299003c9c2312c846
- Size of remote file:
- 264 MB
- SHA256:
- 061cc994ac58cfaec3e77969727ea64051967c0ecf4c79e02fc143fc449ce890
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