---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
metrics:
- accuracy
- precision
- recall
model-index:
- name: llama3-8B_Fact_U
  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. -->

# llama3-8B_Fact_U

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co./meta-llama/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1146
- Accuracy: 0.7953
- Precision: 0.8252
- Recall: 0.7692
- F1 score: 0.7963

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1 score |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.9272        | 0.2509 | 200  | 1.1877          | 0.5765   | 0.5589    | 0.8801 | 0.6837   |
| 0.7133        | 0.5019 | 400  | 0.7246          | 0.7      | 0.6912    | 0.7647 | 0.7261   |
| 0.6476        | 0.7528 | 600  | 0.6219          | 0.7565   | 0.7945    | 0.7172 | 0.7539   |
| 0.5766        | 1.0038 | 800  | 0.5714          | 0.76     | 0.85      | 0.6538 | 0.7391   |
| 0.4292        | 1.2547 | 1000 | 0.5870          | 0.7588   | 0.7474    | 0.8100 | 0.7774   |
| 0.4047        | 1.5056 | 1200 | 0.6078          | 0.7824   | 0.7694    | 0.8303 | 0.7987   |
| 0.4753        | 1.7566 | 1400 | 0.5654          | 0.7929   | 0.8575    | 0.7217 | 0.7838   |
| 0.3831        | 2.0075 | 1600 | 0.5672          | 0.8012   | 0.8289    | 0.7783 | 0.8028   |
| 0.2666        | 2.2585 | 1800 | 0.6054          | 0.8047   | 0.8416    | 0.7692 | 0.8038   |
| 0.3267        | 2.5094 | 2000 | 0.6620          | 0.7929   | 0.8182    | 0.7738 | 0.7953   |
| 0.2742        | 2.7604 | 2200 | 0.6985          | 0.7941   | 0.8579    | 0.7240 | 0.7853   |
| 0.3125        | 3.0113 | 2400 | 0.6373          | 0.7859   | 0.7955    | 0.7919 | 0.7937   |
| 0.1743        | 3.2622 | 2600 | 0.8362          | 0.8047   | 0.8730    | 0.7308 | 0.7956   |
| 0.1837        | 3.5132 | 2800 | 0.7889          | 0.7965   | 0.8440    | 0.7466 | 0.7923   |
| 0.2013        | 3.7641 | 3000 | 0.8958          | 0.7659   | 0.7425    | 0.8416 | 0.7890   |
| 0.1572        | 4.0151 | 3200 | 0.8662          | 0.8012   | 0.864     | 0.7330 | 0.7931   |
| 0.0993        | 4.2660 | 3400 | 0.8730          | 0.8024   | 0.8477    | 0.7557 | 0.7990   |
| 0.0704        | 4.5169 | 3600 | 1.0571          | 0.8012   | 0.8421    | 0.7602 | 0.7990   |
| 0.0598        | 4.7679 | 3800 | 1.1146          | 0.7953   | 0.8252    | 0.7692 | 0.7963   |


### Framework versions

- PEFT 0.11.1
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1