Instructions to use huiwonLee/dpo_v4_reseve_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use huiwonLee/dpo_v4_reseve_v1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("davidkim205/nox-solar-10.7b-v4") model = PeftModel.from_pretrained(base_model, "huiwonLee/dpo_v4_reseve_v1") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - dpo | |
| - generated_from_trainer | |
| base_model: davidkim205/nox-solar-10.7b-v4 | |
| model-index: | |
| - name: dpo_v4_reseve_v1 | |
| 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. --> | |
| # dpo_v4_reseve_v1 | |
| This model is a fine-tuned version of [davidkim205/nox-solar-10.7b-v4](https://huggingface.co/davidkim205/nox-solar-10.7b-v4) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.2529 | |
| - Rewards/chosen: 1.8018 | |
| - Rewards/rejected: -2.8948 | |
| - Rewards/accuracies: 0.5640 | |
| - Rewards/margins: 4.6966 | |
| - Logps/rejected: -431.3928 | |
| - Logps/chosen: -361.0608 | |
| - Logits/rejected: -2.2914 | |
| - Logits/chosen: -2.3061 | |
| ## 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: 0.0001 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | | |
| |:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:| | |
| | 1.5803 | 1.0 | 4750 | 1.2529 | 1.8018 | -2.8948 | 0.5640 | 4.6966 | -431.3928 | -361.0608 | -2.2914 | -2.3061 | | |
| ### Framework versions | |
| - PEFT 0.5.0 | |
| - Transformers 4.39.3 | |
| - Pytorch 2.1.1+cu121 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 | |