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---
license: apache-2.0
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.3
model-index:
- name: Mistral-7B-Instruct-v0.3-dpo-lora_lr1e-5_2ep
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. -->
# Mistral-7B-Instruct-v0.3-dpo-lora_lr1e-5_2ep
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3109
- Rewards/chosen: 0.7129
- Rewards/rejected: -1.3545
- Rewards/accuracies: 0.8795
- Rewards/margins: 2.0674
- Logps/rejected: -373.3019
- Logps/chosen: -422.7253
- Logits/rejected: -0.2832
- Logits/chosen: -0.1040
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.4052 | 1.0 | 103 | 0.3249 | 0.5808 | -1.2724 | 0.8795 | 1.8532 | -372.4805 | -424.0459 | -0.3089 | -0.1139 |
| 0.1566 | 2.0 | 206 | 0.3109 | 0.7129 | -1.3545 | 0.8795 | 2.0674 | -373.3019 | -422.7253 | -0.2832 | -0.1040 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |