---
base_model: unsloth/llama-3-8b
library_name: peft
license: llama3
tags:
- unsloth
- generated_from_trainer
model-index:
- name: Meta-Llama-3-8B_magiccoder_reverse
  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. -->

# Meta-Llama-3-8B_magiccoder_reverse

This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co./unsloth/llama-3-8b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2663

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2593        | 0.0259 | 4    | 1.4180          |
| 1.4127        | 0.0518 | 8    | 1.3958          |
| 1.3964        | 0.0777 | 12   | 1.3814          |
| 1.3824        | 0.1036 | 16   | 1.3679          |
| 1.4044        | 0.1296 | 20   | 1.3733          |
| 1.3092        | 0.1555 | 24   | 1.3639          |
| 1.3823        | 0.1814 | 28   | 1.4075          |
| 1.3878        | 0.2073 | 32   | 1.3768          |
| 1.3653        | 0.2332 | 36   | 1.3592          |
| 1.4395        | 0.2591 | 40   | 1.3594          |
| 1.3805        | 0.2850 | 44   | 1.3444          |
| 1.2631        | 0.3109 | 48   | 1.3354          |
| 1.3346        | 0.3368 | 52   | 1.3409          |
| 1.3776        | 0.3628 | 56   | 1.3367          |
| 1.3407        | 0.3887 | 60   | 1.3291          |
| 1.3939        | 0.4146 | 64   | 1.3307          |
| 1.2555        | 0.4405 | 68   | 1.3257          |
| 1.3227        | 0.4664 | 72   | 1.3216          |
| 1.2664        | 0.4923 | 76   | 1.3238          |
| 1.3542        | 0.5182 | 80   | 1.3157          |
| 1.2873        | 0.5441 | 84   | 1.3167          |
| 1.3065        | 0.5700 | 88   | 1.3109          |
| 1.4021        | 0.5960 | 92   | 1.3056          |
| 1.2277        | 0.6219 | 96   | 1.3021          |
| 1.3014        | 0.6478 | 100  | 1.3000          |
| 1.2138        | 0.6737 | 104  | 1.2911          |
| 1.2367        | 0.6996 | 108  | 1.2903          |
| 1.2501        | 0.7255 | 112  | 1.2844          |
| 1.2942        | 0.7514 | 116  | 1.2819          |
| 1.2762        | 0.7773 | 120  | 1.2780          |
| 1.2871        | 0.8032 | 124  | 1.2753          |
| 1.2829        | 0.8291 | 128  | 1.2724          |
| 1.272         | 0.8551 | 132  | 1.2695          |
| 1.2242        | 0.8810 | 136  | 1.2685          |
| 1.253         | 0.9069 | 140  | 1.2678          |
| 1.2116        | 0.9328 | 144  | 1.2671          |
| 1.2356        | 0.9587 | 148  | 1.2666          |
| 1.2087        | 0.9846 | 152  | 1.2663          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1