---
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- generator
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: mistral_7b_cosine_lr
  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_cosine_lr

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3810

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- lr_scheduler_warmup_steps: 15
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8199        | 0.0366 | 10   | 0.6013          |
| 0.5795        | 0.0732 | 20   | 0.5157          |
| 0.5089        | 0.1098 | 30   | 0.4825          |
| 0.4742        | 0.1465 | 40   | 0.4628          |
| 0.468         | 0.1831 | 50   | 0.4502          |
| 0.4489        | 0.2197 | 60   | 0.4424          |
| 0.4471        | 0.2563 | 70   | 0.4378          |
| 0.4529        | 0.2929 | 80   | 0.4365          |
| 0.4382        | 0.3295 | 90   | 0.4289          |
| 0.4352        | 0.3661 | 100  | 0.4267          |
| 0.4331        | 0.4027 | 110  | 0.4214          |
| 0.4368        | 0.4394 | 120  | 0.4326          |
| 0.4244        | 0.4760 | 130  | 0.4188          |
| 0.4231        | 0.5126 | 140  | 0.4143          |
| 0.418         | 0.5492 | 150  | 0.4103          |
| 0.4118        | 0.5858 | 160  | 0.4066          |
| 0.4151        | 0.6224 | 170  | 0.4047          |
| 0.4093        | 0.6590 | 180  | 0.4020          |
| 0.4066        | 0.6957 | 190  | 0.4004          |
| 0.4072        | 0.7323 | 200  | 0.3970          |
| 0.4002        | 0.7689 | 210  | 0.3933          |
| 0.3968        | 0.8055 | 220  | 0.3919          |
| 0.3967        | 0.8421 | 230  | 0.3893          |
| 0.3901        | 0.8787 | 240  | 0.3870          |
| 0.3895        | 0.9153 | 250  | 0.3852          |
| 0.3929        | 0.9519 | 260  | 0.3823          |
| 0.3854        | 0.9886 | 270  | 0.3808          |
| 0.3524        | 1.0252 | 280  | 0.3833          |
| 0.332         | 1.0618 | 290  | 0.3821          |
| 0.3283        | 1.0984 | 300  | 0.3810          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0