Instructions to use kk-aivio/ff948bfb-81ac-44c6-a4ed-8954f2d0956d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kk-aivio/ff948bfb-81ac-44c6-a4ed-8954f2d0956d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-random-GemmaForCausalLM") model = PeftModel.from_pretrained(base_model, "kk-aivio/ff948bfb-81ac-44c6-a4ed-8954f2d0956d") - Notebooks
- Google Colab
- Kaggle
ff948bfb-81ac-44c6-a4ed-8954f2d0956d
This model is a fine-tuned version of fxmarty/tiny-random-GemmaForCausalLM on the None dataset. It achieves the following results on the evaluation set:
- Loss: 12.3662
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for kk-aivio/ff948bfb-81ac-44c6-a4ed-8954f2d0956d
Base model
fxmarty/tiny-random-GemmaForCausalLM