Text Generation
PEFT
Safetensors
Italian
lora
baseline
flat-training
math
arithmetic
control-group
conversational
Instructions to use dexmac/progressive-cognitive-baseline-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dexmac/progressive-cognitive-baseline-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B") model = PeftModel.from_pretrained(base_model, "dexmac/progressive-cognitive-baseline-lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0c11144d4250fa4b06bbb37a4e4f904a41e3b907a74463355dcedb8aab7a3c17
- Size of remote file:
- 11.4 MB
- SHA256:
- 7ada60cf90bdaa42c848756ca4a39a4142ff7cb5d77b2d47c59c7b19ce5eef1a
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