File size: 2,621 Bytes
85b2766 8651b82 85b2766 8651b82 85b2766 8651b82 85b2766 8651b82 85b2766 0f967c1 85b2766 0f967c1 85b2766 f397dff 85b2766 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
---
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
---
<!-- 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. -->
# 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 |