Instructions to use shibajustfor/b0a2a05e-23f0-4fa7-97d5-312eb1c52a0e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/b0a2a05e-23f0-4fa7-97d5-312eb1c52a0e with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/workspace/input_data/NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer") model = PeftModel.from_pretrained(base_model, "shibajustfor/b0a2a05e-23f0-4fa7-97d5-312eb1c52a0e") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: NousResearch/Meta-Llama-3-8B-Alternate-Tokenizer | |
| model-index: | |
| - name: shibajustfor/b0a2a05e-23f0-4fa7-97d5-312eb1c52a0e | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # shibajustfor/b0a2a05e-23f0-4fa7-97d5-312eb1c52a0e | |
| This model was trained from scratch on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1378 | |
| ## 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 |