Instructions to use shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/Intel/neural-chat-7b-v3-3") model = PeftModel.from_pretrained(base_model, "shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe") - Notebooks
- Google Colab
- Kaggle
shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2243
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
- 2
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Model tree for shibajustfor/11fa1945-dd31-4829-a3c7-7717a7e32cbe
Base model
mistralai/Mistral-7B-v0.1 Finetuned
Intel/neural-chat-7b-v3-1 Finetuned
Intel/neural-chat-7b-v3-3