Instructions to use baby-dev/0c3244ca-3f10-458f-b57d-cae7aea2fbc2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use baby-dev/0c3244ca-3f10-458f-b57d-cae7aea2fbc2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4") model = PeftModel.from_pretrained(base_model, "baby-dev/0c3244ca-3f10-458f-b57d-cae7aea2fbc2") - Notebooks
- Google Colab
- Kaggle
0c3244ca-3f10-458f-b57d-cae7aea2fbc2
This model is a fine-tuned version of MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.8205
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 baby-dev/0c3244ca-3f10-458f-b57d-cae7aea2fbc2
Base model
MNC-Jihun/Mistral-7B-AO-u0.5-b2-ver0.4