Instructions to use havinash-ai/e0350b39-7f18-402e-b230-c70993440cb2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use havinash-ai/e0350b39-7f18-402e-b230-c70993440cb2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Capybara-7B-V1.9") model = PeftModel.from_pretrained(base_model, "havinash-ai/e0350b39-7f18-402e-b230-c70993440cb2") - Notebooks
- Google Colab
- Kaggle
e0350b39-7f18-402e-b230-c70993440cb2
This model is a fine-tuned version of NousResearch/Nous-Capybara-7B-V1.9 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7212
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
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Model tree for havinash-ai/e0350b39-7f18-402e-b230-c70993440cb2
Base model
NousResearch/Nous-Capybara-7B-V1.9