Instructions to use Katochh/GenAI-task2-ModelB-DS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Katochh/GenAI-task2-ModelB-DS with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("petals-team/falcon-rw-1b") model = PeftModel.from_pretrained(base_model, "Katochh/GenAI-task2-ModelB-DS") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: petals-team/falcon-rw-1b | |
| model-index: | |
| - name: GenAI-task2-ModelB-DS | |
| 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. --> | |
| # GenAI-task2-ModelB-DS | |
| This model is a fine-tuned version of [petals-team/falcon-rw-1b](https://huggingface.co/petals-team/falcon-rw-1b) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7129 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 2e-05 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.01 | |
| - num_epochs: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.384 | 0.1 | 20 | 1.3865 | | |
| | 1.3566 | 0.2 | 40 | 1.2177 | | |
| | 1.1994 | 0.3 | 60 | 1.0759 | | |
| | 1.1201 | 0.4 | 80 | 0.9830 | | |
| | 0.8714 | 0.5 | 100 | 0.9083 | | |
| | 0.9164 | 0.6 | 120 | 0.8488 | | |
| | 0.7921 | 0.7 | 140 | 0.8116 | | |
| | 0.7861 | 0.8 | 160 | 0.7901 | | |
| | 0.8395 | 0.9 | 180 | 0.7645 | | |
| | 0.5803 | 1.0 | 200 | 0.7605 | | |
| | 0.8115 | 1.1 | 220 | 0.7420 | | |
| | 0.687 | 1.2 | 240 | 0.7307 | | |
| | 0.6314 | 1.3 | 260 | 0.7258 | | |
| | 0.8237 | 1.4 | 280 | 0.7206 | | |
| | 0.5456 | 1.5 | 300 | 0.7218 | | |
| | 0.8775 | 1.6 | 320 | 0.7158 | | |
| | 0.6114 | 1.7 | 340 | 0.7134 | | |
| | 0.668 | 1.8 | 360 | 0.7137 | | |
| | 0.756 | 1.9 | 380 | 0.7130 | | |
| | 0.5245 | 2.0 | 400 | 0.7129 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.1+cu121 | |
| - Datasets 2.19.0 | |
| - Tokenizers 0.19.1 |