---
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-iter1/50_new
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-iter1/50_new dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4740
- Rewards/chosen: -0.4546
- Rewards/rejected: -1.1932
- Rewards/accuracies: 0.8036
- Rewards/margins: 0.7386
- Logps/rejected: -459.2464
- Logps/chosen: -346.3625
- Logits/rejected All: -2.7774
- Logits/chosen All: -2.7702
- Logits/rejected Sum: 8023.3535
- Logits/chosen Sum: 8554.5498
- Logits/rejected Avg: 21.6078
- Logits/chosen Avg: 21.0986
- Gradient/inner Product: 463470592.0
- Gradient/nabla Chosen Logps: 28288.0
- Gradient/nabla Rejected Logps: 37632.0
- Gradient/correlation: 0.4004

## 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             | -339.9275      | -300.9012    | -2.8672             | -2.8605           | 7351.9551           | 7878.5537         | 19.8359             | 19.5574           | 86507520.0             | 16384.0                     | 17152.0                       | 0.2451               |
| 0.667         | 0.6803 | 100  | 0.4740          | -0.4546        | -1.1932          | 0.8036             | 0.7386          | -459.2464      | -346.3625    | -2.7774             | -2.7702           | 8023.3535           | 8554.5498         | 21.6078             | 21.0986           | 463470592.0            | 28288.0                     | 37632.0                       | 0.4004               |


### Framework versions

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