Instructions to use shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/NousResearch/Yarn-Mistral-7b-128k") model = PeftModel.from_pretrained(base_model, "shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
tags:
- generated_from_trainer
base_model: NousResearch/Yarn-Mistral-7b-128k
model-index:
- name: shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef
results: []
shibajustfor/5a67588d-8c19-4508-a315-f804ea005cef
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3707
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