Instructions to use shibajustfor/2e99a493-4a46-49a1-99b4-b605cd91e9b5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/2e99a493-4a46-49a1-99b4-b605cd91e9b5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codegemma-7b-it") model = PeftModel.from_pretrained(base_model, "shibajustfor/2e99a493-4a46-49a1-99b4-b605cd91e9b5") - Notebooks
- Google Colab
- Kaggle
2e99a493-4a46-49a1-99b4-b605cd91e9b5
This model is a fine-tuned version of unsloth/codegemma-7b-it on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.0131
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
- Downloads last month
- 3
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Model tree for shibajustfor/2e99a493-4a46-49a1-99b4-b605cd91e9b5
Base model
unsloth/codegemma-7b-it