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---
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 |