---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Llama-3.2-3B-Instruct-JEP
  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. -->

# Llama-3.2-3B-Instruct-JEP

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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1199

## 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4921        | 0.1535 | 100  | 1.4430          |
| 1.2984        | 0.3070 | 200  | 1.3138          |
| 1.2315        | 0.4605 | 300  | 1.2702          |
| 1.2217        | 0.6140 | 400  | 1.2429          |
| 1.263         | 0.7675 | 500  | 1.2280          |
| 1.1829        | 0.9210 | 600  | 1.2155          |
| 1.1802        | 1.0737 | 700  | 1.2063          |
| 1.2061        | 1.2272 | 800  | 1.1995          |
| 1.1387        | 1.3807 | 900  | 1.1922          |
| 1.1639        | 1.5342 | 1000 | 1.1858          |
| 1.1446        | 1.6876 | 1100 | 1.1828          |
| 1.1536        | 1.8411 | 1200 | 1.1768          |
| 1.1929        | 1.9946 | 1300 | 1.1715          |
| 1.1902        | 2.1474 | 1400 | 1.1703          |
| 1.165         | 2.3008 | 1500 | 1.1661          |
| 1.146         | 2.4543 | 1600 | 1.1634          |
| 1.1346        | 2.6078 | 1700 | 1.1604          |
| 1.1227        | 2.7613 | 1800 | 1.1571          |
| 1.1103        | 2.9148 | 1900 | 1.1537          |
| 1.0672        | 3.0675 | 2000 | 1.1522          |
| 1.1103        | 3.2210 | 2100 | 1.1514          |
| 1.1034        | 3.3745 | 2200 | 1.1489          |
| 1.1958        | 3.5280 | 2300 | 1.1459          |
| 1.1257        | 3.6815 | 2400 | 1.1447          |
| 1.0882        | 3.8350 | 2500 | 1.1435          |
| 1.1452        | 3.9885 | 2600 | 1.1427          |
| 1.1185        | 4.1412 | 2700 | 1.1423          |
| 1.0371        | 4.2947 | 2800 | 1.1410          |
| 1.1447        | 4.4482 | 2900 | 1.1386          |
| 1.0647        | 4.6017 | 3000 | 1.1368          |
| 1.0847        | 4.7552 | 3100 | 1.1355          |
| 1.0754        | 4.9087 | 3200 | 1.1337          |
| 1.0596        | 5.0614 | 3300 | 1.1335          |
| 1.127         | 5.2149 | 3400 | 1.1337          |
| 1.0244        | 5.3684 | 3500 | 1.1325          |
| 1.1621        | 5.5219 | 3600 | 1.1310          |
| 1.102         | 5.6754 | 3700 | 1.1305          |
| 1.1348        | 5.8289 | 3800 | 1.1289          |
| 1.0641        | 5.9823 | 3900 | 1.1280          |
| 1.0889        | 6.1351 | 4000 | 1.1276          |
| 1.0684        | 6.2886 | 4100 | 1.1274          |
| 1.0523        | 6.4421 | 4200 | 1.1264          |
| 1.0437        | 6.5955 | 4300 | 1.1265          |
| 1.0645        | 6.7490 | 4400 | 1.1256          |
| 1.0412        | 6.9025 | 4500 | 1.1248          |
| 1.0904        | 7.0553 | 4600 | 1.1248          |
| 1.0982        | 7.2087 | 4700 | 1.1249          |
| 1.0802        | 7.3622 | 4800 | 1.1246          |
| 1.0236        | 7.5157 | 4900 | 1.1236          |
| 1.0447        | 7.6692 | 5000 | 1.1224          |
| 1.0408        | 7.8227 | 5100 | 1.1219          |
| 1.1236        | 7.9762 | 5200 | 1.1215          |
| 1.0381        | 8.1289 | 5300 | 1.1216          |
| 1.0971        | 8.2824 | 5400 | 1.1212          |
| 1.0529        | 8.4359 | 5500 | 1.1211          |
| 0.966         | 8.5894 | 5600 | 1.1214          |
| 1.0575        | 8.7429 | 5700 | 1.1205          |
| 1.0836        | 8.8964 | 5800 | 1.1201          |
| 0.997         | 9.0491 | 5900 | 1.1207          |
| 1.0106        | 9.2026 | 6000 | 1.1204          |
| 1.0164        | 9.3561 | 6100 | 1.1203          |
| 1.0576        | 9.5096 | 6200 | 1.1203          |
| 1.0619        | 9.6631 | 6300 | 1.1199          |
| 1.0607        | 9.8166 | 6400 | 1.1201          |
| 0.9816        | 9.9701 | 6500 | 1.1199          |


### Framework versions

- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1