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---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: unaligned
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. -->
# unaligned
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0709
## 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: 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: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 2048
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1154 | 0.1110 | 100 | 0.1172 |
| 0.092 | 0.2220 | 200 | 0.1028 |
| 0.0462 | 0.3330 | 300 | 0.0992 |
| 0.0482 | 0.4440 | 400 | 0.0755 |
| 0.043 | 0.5550 | 500 | 0.0794 |
| 0.0476 | 0.6660 | 600 | 0.0628 |
| 0.0482 | 0.7770 | 700 | 0.0821 |
| 0.0484 | 0.8880 | 800 | 0.0691 |
| 0.0448 | 0.9990 | 900 | 0.0829 |
| 0.0214 | 1.1100 | 1000 | 0.0720 |
| 0.0439 | 1.2210 | 1100 | 0.0635 |
| 0.0364 | 1.3320 | 1200 | 0.0713 |
| 0.0497 | 1.4430 | 1300 | 0.0669 |
| 0.0455 | 1.5540 | 1400 | 0.0672 |
| 0.0614 | 1.6650 | 1500 | 0.0805 |
| 0.0416 | 1.7761 | 1600 | 0.0669 |
| 0.0367 | 1.8871 | 1700 | 0.0716 |
| 0.0578 | 1.9981 | 1800 | 0.0684 |
| 0.0358 | 2.1091 | 1900 | 0.0705 |
| 0.0326 | 2.2201 | 2000 | 0.0709 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |