---
library_name: peft
license: other
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- llama-factory
- lora
- unsloth
- generated_from_trainer
model-index:
- name: llm3br256-v1.5
  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. -->

# llm3br256-v1.5

This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the asianpaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0157

## 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: 0.0001
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1302        | 0.1208 | 25   | 0.1429          |
| 0.0909        | 0.2415 | 50   | 0.0968          |
| 0.062         | 0.3623 | 75   | 0.0748          |
| 0.0602        | 0.4831 | 100  | 0.0608          |
| 0.057         | 0.6039 | 125  | 0.0552          |
| 0.0456        | 0.7246 | 150  | 0.0501          |
| 0.0432        | 0.8454 | 175  | 0.0470          |
| 0.0416        | 0.9662 | 200  | 0.0447          |
| 0.0441        | 1.0870 | 225  | 0.0428          |
| 0.0348        | 1.2077 | 250  | 0.0405          |
| 0.0355        | 1.3285 | 275  | 0.0386          |
| 0.0379        | 1.4493 | 300  | 0.0358          |
| 0.032         | 1.5700 | 325  | 0.0354          |
| 0.0342        | 1.6908 | 350  | 0.0335          |
| 0.0318        | 1.8116 | 375  | 0.0324          |
| 0.031         | 1.9324 | 400  | 0.0318          |
| 0.0283        | 2.0531 | 425  | 0.0321          |
| 0.0275        | 2.1739 | 450  | 0.0337          |
| 0.026         | 2.2947 | 475  | 0.0314          |
| 0.0244        | 2.4155 | 500  | 0.0285          |
| 0.0281        | 2.5362 | 525  | 0.0285          |
| 0.0212        | 2.6570 | 550  | 0.0268          |
| 0.0221        | 2.7778 | 575  | 0.0267          |
| 0.0225        | 2.8986 | 600  | 0.0266          |
| 0.0264        | 3.0193 | 625  | 0.0292          |
| 0.0196        | 3.1401 | 650  | 0.0280          |
| 0.0185        | 3.2609 | 675  | 0.0264          |
| 0.0161        | 3.3816 | 700  | 0.0248          |
| 0.0186        | 3.5024 | 725  | 0.0226          |
| 0.0166        | 3.6232 | 750  | 0.0213          |
| 0.0141        | 3.7440 | 775  | 0.0215          |
| 0.0186        | 3.8647 | 800  | 0.0211          |
| 0.0119        | 3.9855 | 825  | 0.0204          |
| 0.0097        | 4.1063 | 850  | 0.0210          |
| 0.0095        | 4.2271 | 875  | 0.0204          |
| 0.0119        | 4.3478 | 900  | 0.0207          |
| 0.0131        | 4.4686 | 925  | 0.0257          |
| 0.0123        | 4.5894 | 950  | 0.0228          |
| 0.0133        | 4.7101 | 975  | 0.0204          |
| 0.0115        | 4.8309 | 1000 | 0.0191          |
| 0.0152        | 4.9517 | 1025 | 0.0201          |
| 0.0075        | 5.0725 | 1050 | 0.0188          |
| 0.0069        | 5.1932 | 1075 | 0.0169          |
| 0.0073        | 5.3140 | 1100 | 0.0182          |
| 0.0076        | 5.4348 | 1125 | 0.0166          |
| 0.0084        | 5.5556 | 1150 | 0.0173          |
| 0.0091        | 5.6763 | 1175 | 0.0175          |
| 0.0081        | 5.7971 | 1200 | 0.0176          |
| 0.0071        | 5.9179 | 1225 | 0.0175          |
| 0.0058        | 6.0386 | 1250 | 0.0187          |
| 0.0081        | 6.1594 | 1275 | 0.0165          |
| 0.0057        | 6.2802 | 1300 | 0.0171          |
| 0.0068        | 6.4010 | 1325 | 0.0165          |
| 0.0059        | 6.5217 | 1350 | 0.0163          |
| 0.0057        | 6.6425 | 1375 | 0.0151          |
| 0.0061        | 6.7633 | 1400 | 0.0164          |
| 0.006         | 6.8841 | 1425 | 0.0156          |
| 0.0062        | 7.0048 | 1450 | 0.0161          |
| 0.006         | 7.1256 | 1475 | 0.0178          |
| 0.0059        | 7.2464 | 1500 | 0.0169          |
| 0.0043        | 7.3671 | 1525 | 0.0175          |
| 0.0049        | 7.4879 | 1550 | 0.0178          |
| 0.0058        | 7.6087 | 1575 | 0.0156          |
| 0.0062        | 7.7295 | 1600 | 0.0158          |
| 0.0045        | 7.8502 | 1625 | 0.0151          |
| 0.0054        | 7.9710 | 1650 | 0.0150          |
| 0.0042        | 8.0918 | 1675 | 0.0157          |
| 0.0039        | 8.2126 | 1700 | 0.0157          |
| 0.0046        | 8.3333 | 1725 | 0.0170          |
| 0.0025        | 8.4541 | 1750 | 0.0154          |
| 0.0047        | 8.5749 | 1775 | 0.0156          |
| 0.0044        | 8.6957 | 1800 | 0.0166          |
| 0.0031        | 8.8164 | 1825 | 0.0172          |
| 0.0029        | 8.9372 | 1850 | 0.0167          |
| 0.0032        | 9.0580 | 1875 | 0.0169          |
| 0.0036        | 9.1787 | 1900 | 0.0167          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.4.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3