Instructions to use shibajustfor/93b88589-59f8-4469-9b0e-6c3faa7275b8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/93b88589-59f8-4469-9b0e-6c3faa7275b8 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/codegemma-7b") model = PeftModel.from_pretrained(base_model, "shibajustfor/93b88589-59f8-4469-9b0e-6c3faa7275b8") - Notebooks
- Google Colab
- Kaggle
93b88589-59f8-4469-9b0e-6c3faa7275b8
This model is a fine-tuned version of unsloth/codegemma-7b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6331
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
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Model tree for shibajustfor/93b88589-59f8-4469-9b0e-6c3faa7275b8
Base model
unsloth/codegemma-7b