---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- base_model:adapter:meta-llama/Meta-Llama-3-8B
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: Llama-3-8bLoRA-Medical2
  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-3-8bLoRA-Medical2

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4624

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4736        | 0.2667 | 100  | 0.4776          |
| 0.4546        | 0.5333 | 200  | 0.4697          |
| 0.4659        | 0.8    | 300  | 0.4645          |
| 0.4357        | 1.0667 | 400  | 0.4637          |
| 0.4163        | 1.3333 | 500  | 0.4643          |
| 0.4228        | 1.6    | 600  | 0.4624          |
| 0.4096        | 1.8667 | 700  | 0.4608          |
| 0.3708        | 2.1333 | 800  | 0.4737          |
| 0.355         | 2.4    | 900  | 0.4737          |
| 0.3693        | 2.6667 | 1000 | 0.4733          |
| 0.3642        | 2.9333 | 1100 | 0.4726          |


### Framework versions

- PEFT 0.17.1
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0