Instructions to use shibajustfor/90731a28-1576-477c-b67f-2bbc0754f0f9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/90731a28-1576-477c-b67f-2bbc0754f0f9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-SOLAR-10.7B") model = PeftModel.from_pretrained(base_model, "shibajustfor/90731a28-1576-477c-b67f-2bbc0754f0f9") - Notebooks
- Google Colab
- Kaggle
90731a28-1576-477c-b67f-2bbc0754f0f9
This model is a fine-tuned version of NousResearch/Nous-Hermes-2-SOLAR-10.7B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2631
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/90731a28-1576-477c-b67f-2bbc0754f0f9
Base model
upstage/SOLAR-10.7B-v1.0 Finetuned
NousResearch/Nous-Hermes-2-SOLAR-10.7B