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

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: 1.2651

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.2463        | 0.0259 | 4    | 1.4033          |
| 1.3247        | 0.0518 | 8    | 1.3322          |
| 1.2964        | 0.0777 | 12   | 1.3196          |
| 1.3289        | 0.1036 | 16   | 1.3224          |
| 1.3483        | 0.1296 | 20   | 1.3100          |
| 1.2491        | 0.1555 | 24   | 1.3194          |
| 1.3072        | 0.1814 | 28   | 1.3144          |
| 1.2865        | 0.2073 | 32   | 1.3123          |
| 1.3102        | 0.2332 | 36   | 1.3171          |
| 1.3752        | 0.2591 | 40   | 1.3158          |
| 1.3244        | 0.2850 | 44   | 1.3114          |
| 1.2311        | 0.3109 | 48   | 1.3118          |
| 1.2911        | 0.3368 | 52   | 1.3135          |
| 1.3409        | 0.3628 | 56   | 1.3079          |
| 1.3069        | 0.3887 | 60   | 1.3043          |
| 1.362         | 0.4146 | 64   | 1.3125          |
| 1.2206        | 0.4405 | 68   | 1.3051          |
| 1.2838        | 0.4664 | 72   | 1.2986          |
| 1.2348        | 0.4923 | 76   | 1.3073          |
| 1.3171        | 0.5182 | 80   | 1.2922          |
| 1.2556        | 0.5441 | 84   | 1.2965          |
| 1.2803        | 0.5700 | 88   | 1.2911          |
| 1.3796        | 0.5960 | 92   | 1.2854          |
| 1.2047        | 0.6219 | 96   | 1.2871          |
| 1.2821        | 0.6478 | 100  | 1.2866          |
| 1.2012        | 0.6737 | 104  | 1.2838          |
| 1.2116        | 0.6996 | 108  | 1.2799          |
| 1.23          | 0.7255 | 112  | 1.2750          |
| 1.2679        | 0.7514 | 116  | 1.2715          |
| 1.2573        | 0.7773 | 120  | 1.2714          |
| 1.2802        | 0.8032 | 124  | 1.2692          |
| 1.2772        | 0.8291 | 128  | 1.2681          |
| 1.2594        | 0.8551 | 132  | 1.2669          |
| 1.218         | 0.8810 | 136  | 1.2666          |
| 1.2391        | 0.9069 | 140  | 1.2658          |
| 1.2084        | 0.9328 | 144  | 1.2656          |
| 1.2245        | 0.9587 | 148  | 1.2651          |
| 1.1995        | 0.9846 | 152  | 1.2651          |


### Framework versions

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