---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: QA-Llama-3.1-4155
  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. -->

# QA-Llama-3.1-4155

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0781
- Accuracy: 0.6965
- Macro F1: 0.6444
- Macro Precision: 0.7361
- Macro Recall: 0.5968
- Micro F1: 0.7539
- Micro Precision: 0.8035
- Micro Recall: 0.7100
- Flagged/accuracy: 0.8561
- Flagged/precision: 0.9050
- Flagged/recall: 0.8284
- Flagged/f1: 0.8650

## 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: 2.8362564501611134e-07
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 128
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Macro F1 | Macro Precision | Macro Recall | Micro F1 | Micro Precision | Micro Recall | Flagged/accuracy | Flagged/precision | Flagged/recall | Flagged/f1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|
| 0.0688        | 1.0   | 8454  | 0.0799          | 0.6891   | 0.6367   | 0.7276          | 0.5931       | 0.7464   | 0.8015          | 0.6984       | 0.8491           | 0.8948            | 0.8260         | 0.8590     |
| 0.0745        | 2.0   | 16908 | 0.0777          | 0.6956   | 0.6295   | 0.7647          | 0.5680       | 0.7503   | 0.8171          | 0.6935       | 0.8532           | 0.9108            | 0.8160         | 0.8608     |
| 0.06          | 3.0   | 25362 | 0.0781          | 0.6965   | 0.6444   | 0.7361          | 0.5968       | 0.7539   | 0.8035          | 0.7100       | 0.8561           | 0.9050            | 0.8284         | 0.8650     |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu118
- Datasets 3.5.1
- Tokenizers 0.21.1