Instructions to use adammandic87/1be6c54b-b42a-4947-97a9-96102f562ce9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/1be6c54b-b42a-4947-97a9-96102f562ce9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Hermes-2-Theta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "adammandic87/1be6c54b-b42a-4947-97a9-96102f562ce9") - Notebooks
- Google Colab
- Kaggle
1be6c54b-b42a-4947-97a9-96102f562ce9
This model is a fine-tuned version of NousResearch/Hermes-2-Theta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1881
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 adammandic87/1be6c54b-b42a-4947-97a9-96102f562ce9
Base model
NousResearch/Meta-Llama-3-8B Finetuned
NousResearch/Hermes-2-Pro-Llama-3-8B Finetuned
NousResearch/Hermes-2-Theta-Llama-3-8B