---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: llama-3.2-3b-medical-dataset-fine-tuned
  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-medical-dataset-fine-tuned

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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8519

## 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: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2124        | 0.0465 | 100  | 2.2831          |
| 2.0834        | 0.0930 | 200  | 2.1037          |
| 2.0729        | 0.1394 | 300  | 2.0504          |
| 1.881         | 0.1859 | 400  | 2.0172          |
| 1.9543        | 0.2324 | 500  | 1.9913          |
| 1.9713        | 0.2789 | 600  | 1.9725          |
| 1.9492        | 0.3254 | 700  | 1.9590          |
| 1.9655        | 0.3718 | 800  | 1.9478          |
| 2.0255        | 0.4183 | 900  | 1.9369          |
| 1.9839        | 0.4648 | 1000 | 1.9279          |
| 1.8153        | 0.5113 | 1100 | 1.9212          |
| 2.069         | 0.5578 | 1200 | 1.9156          |
| 1.8085        | 0.6042 | 1300 | 1.9107          |
| 1.8947        | 0.6507 | 1400 | 1.9061          |
| 1.8708        | 0.6972 | 1500 | 1.9022          |
| 1.8659        | 0.7437 | 1600 | 1.8984          |
| 1.951         | 0.7901 | 1700 | 1.8951          |
| 1.9871        | 0.8366 | 1800 | 1.8917          |
| 1.8627        | 0.8831 | 1900 | 1.8892          |
| 1.8984        | 0.9296 | 2000 | 1.8865          |
| 1.9381        | 0.9761 | 2100 | 1.8838          |
| 1.8315        | 1.0225 | 2200 | 1.8819          |
| 1.9927        | 1.0690 | 2300 | 1.8797          |
| 1.7257        | 1.1155 | 2400 | 1.8783          |
| 1.9064        | 1.1620 | 2500 | 1.8762          |
| 1.8463        | 1.2085 | 2600 | 1.8744          |
| 1.864         | 1.2549 | 2700 | 1.8728          |
| 1.8915        | 1.3014 | 2800 | 1.8714          |
| 1.8045        | 1.3479 | 2900 | 1.8698          |
| 1.7097        | 1.3944 | 3000 | 1.8688          |
| 1.8884        | 1.4409 | 3100 | 1.8672          |
| 1.9608        | 1.4873 | 3200 | 1.8657          |
| 1.9233        | 1.5338 | 3300 | 1.8645          |
| 1.908         | 1.5803 | 3400 | 1.8637          |
| 1.8181        | 1.6268 | 3500 | 1.8624          |
| 1.7803        | 1.6733 | 3600 | 1.8614          |
| 1.8635        | 1.7197 | 3700 | 1.8603          |
| 1.763         | 1.7662 | 3800 | 1.8596          |
| 1.7074        | 1.8127 | 3900 | 1.8588          |
| 1.7098        | 1.8592 | 4000 | 1.8579          |
| 1.7753        | 1.9056 | 4100 | 1.8574          |
| 1.8458        | 1.9521 | 4200 | 1.8567          |
| 1.8413        | 1.9986 | 4300 | 1.8560          |
| 1.8139        | 2.0451 | 4400 | 1.8557          |
| 1.813         | 2.0916 | 4500 | 1.8554          |
| 1.8516        | 2.1380 | 4600 | 1.8550          |
| 1.7899        | 2.1845 | 4700 | 1.8545          |
| 1.8442        | 2.2310 | 4800 | 1.8541          |
| 1.9263        | 2.2775 | 4900 | 1.8538          |
| 1.8216        | 2.3240 | 5000 | 1.8534          |
| 1.6517        | 2.3704 | 5100 | 1.8531          |
| 1.7538        | 2.4169 | 5200 | 1.8530          |
| 1.7886        | 2.4634 | 5300 | 1.8526          |
| 1.7547        | 2.5099 | 5400 | 1.8525          |
| 1.8083        | 2.5564 | 5500 | 1.8524          |
| 1.7888        | 2.6028 | 5600 | 1.8524          |
| 1.6415        | 2.6493 | 5700 | 1.8522          |
| 1.6981        | 2.6958 | 5800 | 1.8521          |
| 1.8211        | 2.7423 | 5900 | 1.8520          |
| 1.7189        | 2.7888 | 6000 | 1.8519          |
| 1.7251        | 2.8352 | 6100 | 1.8519          |
| 1.8117        | 2.8817 | 6200 | 1.8518          |
| 1.8117        | 2.9282 | 6300 | 1.8519          |
| 1.9351        | 2.9747 | 6400 | 1.8519          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3