---
base_model: mistralai/Mistral-7B-Instruct-v0.1
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-QLoRA
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dhanishetty-personaluse/huggingface/runs/pjjtau6u)
# Mistral-QLoRA

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7712

## 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-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0043        | 0.2022 | 100  | 1.0068          |
| 0.8452        | 0.4044 | 200  | 0.8355          |
| 0.7876        | 0.6067 | 300  | 0.8093          |
| 0.8128        | 0.8089 | 400  | 0.7969          |
| 0.747         | 1.0111 | 500  | 0.7895          |
| 0.744         | 1.2133 | 600  | 0.7853          |
| 0.7757        | 1.4156 | 700  | 0.7797          |
| 0.7342        | 1.6178 | 800  | 0.7763          |
| 0.7338        | 1.8200 | 900  | 0.7736          |
| 0.707         | 2.0222 | 1000 | 0.7737          |
| 0.7021        | 2.2245 | 1100 | 0.7756          |
| 0.7102        | 2.4267 | 1200 | 0.7742          |
| 0.697         | 2.6289 | 1300 | 0.7735          |
| 0.7155        | 2.8311 | 1400 | 0.7712          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.42.4
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1