Instructions to use PaulD/null with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use PaulD/null with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "PaulD/null") - Notebooks
- Google Colab
- Kaggle
| base_model: meta-llama/Meta-Llama-3-8B-Instruct | |
| library_name: peft | |
| license: llama3 | |
| tags: | |
| - trl | |
| - kto | |
| - generated_from_trainer | |
| model-index: | |
| - name: 'null' | |
| 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. --> | |
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/pauld/huggingface/runs/5ep5fter) | |
| # null | |
| This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6004 | |
| - Eval/rewards/chosen: 0.0713 | |
| - Eval/logps/chosen: -174.6075 | |
| - Eval/rewards/rejected: 0.0986 | |
| - Eval/logps/rejected: -217.2799 | |
| - Eval/rewards/margins: -0.0273 | |
| - Eval/kl: 0.7783 | |
| ## 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: 1e-05 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 5.0 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | | |
| |:-------------:|:------:|:----:|:---------------:|:------:| | |
| | 0.5651 | 0.9677 | 15 | 0.6026 | 0.1513 | | |
| | 0.5618 | 2.0 | 31 | 0.5999 | 0.3742 | | |
| | 0.5484 | 2.9677 | 46 | 0.6006 | 0.6711 | | |
| | 0.5466 | 4.0 | 62 | 0.6003 | 0.8158 | | |
| | 0.6017 | 4.8387 | 75 | 0.6004 | 0.7783 | | |
| ### Framework versions | |
| - PEFT 0.11.1 | |
| - Transformers 4.42.2 | |
| - Pytorch 2.2.0 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 |