---
base_model: openaccess-ai-collective/tiny-mistral
library_name: peft
tags:
- generated_from_trainer
- fine-tuning
- text-generation
model-index:
- name: tiny-mistral-alpaca-finance
  results: []
datasets:
- gbharti/finance-alpaca
---

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# Tiny Mistral fine-tuned on finance dataset

This model is a fine-tuned version of the `openaccess-ai-collective/tiny-mistral` language model. 
It has been fine-tuned on a specialized finance dataset using Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA). 
The model is designed to generate responses based on financial instructions and contexts.

## Intended uses & limitations

This model is intended for text generation tasks specifically related to financial instructions and contexts. 
It can be used for generating responses based on given financial prompts.

**Limitations:**
- The model may not perform well on financial topics not covered in the training data.
- The quality of responses may vary depending on the specificity and complexity of the financial queries.
- The model may generate responses that are not factually accurate or may include biases present in the training data.

## Training and evaluation data

The model was fine-tuned on the `gbharti/finance-alpaca` dataset, which includes financial instructions and outputs. 
The dataset was processed to format instructions with or without additional context. 

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3155        | 0.2580 | 500  | 1.3207          |
| 1.1306        | 0.5160 | 1000 | 1.1318          |
| 0.9935        | 0.7739 | 1500 | 0.9970          |
| 0.7188        | 1.0319 | 2000 | 0.8934          |
| 0.6962        | 1.2899 | 2500 | 0.8238          |
| 0.6427        | 1.5479 | 3000 | 0.7610          |
| 0.6014        | 1.8059 | 3500 | 0.7193          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1