---
library_name: transformers
base_model: /scratch/gpfs/jg9904/saved_models/Mistral-7B-Instruct-v0.3
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- /scratch/gpfs/jg9904/unintentional-unalignment/data_files/data-mistral-7b-instruct-sppo-iter1/90_variance
model-index:
- name: mistral-dpo-lr-5.0e-7-beta-0.01
  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-dpo-lr-5.0e-7-beta-0.01

This model is a fine-tuned version of [/scratch/gpfs/jg9904/saved_models/Mistral-7B-Instruct-v0.3](https://huggingface.co//scratch/gpfs/jg9904/saved_models/Mistral-7B-Instruct-v0.3) on the /scratch/gpfs/jg9904/unintentional-unalignment/data_files/data-mistral-7b-instruct-sppo-iter1/90_variance dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4177
- Rewards/chosen: -0.6130
- Rewards/rejected: -1.5691
- Rewards/accuracies: 0.8741
- Rewards/margins: 0.9561
- Logps/rejected: -497.8502
- Logps/chosen: -361.6783
- Logits/rejected All: -2.7429
- Logits/chosen All: -2.7344
- Logits/rejected Sum: 8021.8809
- Logits/chosen Sum: 8575.8633
- Logits/rejected Avg: 21.7435
- Logits/chosen Avg: 21.2499
- Gradient/inner Product: 1124073472.0
- Gradient/nabla Chosen Logps: 37120.0
- Gradient/nabla Rejected Logps: 45824.0
- Gradient/correlation: 0.4668

## 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: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- 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 All | Logits/chosen All | Logits/rejected Sum | Logits/chosen Sum | Logits/rejected Avg | Logits/chosen Avg | Gradient/inner Product | Gradient/nabla Chosen Logps | Gradient/nabla Rejected Logps | Gradient/correlation |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:-------------------:|:-----------------:|:-------------------:|:-----------------:|:-------------------:|:-----------------:|:----------------------:|:---------------------------:|:-----------------------------:|:--------------------:|
| No log        | 0      | 0    | 0.6931          | 0.0            | 0.0              | 0.0                | 0.0             | -340.9357      | -300.3766    | -2.8879             | -2.8885           | 7277.5859           | 7814.5264         | 19.8154             | 19.5578           | 77594624.0             | 16256.0                     | 17152.0                       | 0.2236               |
| 0.6346        | 0.3788 | 100  | 0.5216          | -0.5880        | -1.1850          | 0.7396             | 0.5970          | -459.4361      | -359.1805    | -2.8931             | -2.9041           | 7889.0952           | 8463.7900         | 21.4027             | 21.0064           | 562036736.0            | 34304.0                     | 41984.0                       | 0.4043               |
| 0.6253        | 0.7576 | 200  | 0.4177          | -0.6130        | -1.5691          | 0.8741             | 0.9561          | -497.8502      | -361.6783    | -2.7429             | -2.7344           | 8021.8809           | 8575.8633         | 21.7435             | 21.2499           | 1124073472.0           | 37120.0                     | 45824.0                       | 0.4668               |


### Framework versions

- Transformers 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 2.14.6
- Tokenizers 0.20.4