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pmrster/test-llama3-8b-instruct-qlora-fine-tune-1
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---
license: llama3
library_name: peft
tags:
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: llama3-8b-instruct-journal-finetune
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-instruct-journal-finetune
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: 2.0299
## 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 2.9002 | 2.0833 | 25 | 1.8777 |
| 1.1645 | 4.1667 | 50 | 1.6214 |
| 0.4078 | 6.25 | 75 | 1.7856 |
| 0.2373 | 8.3333 | 100 | 1.8434 |
| 0.2209 | 10.4167 | 125 | 1.7767 |
| 0.1953 | 12.5 | 150 | 1.8293 |
| 0.1755 | 14.5833 | 175 | 1.7663 |
| 0.1893 | 16.6667 | 200 | 1.8726 |
| 0.1621 | 18.75 | 225 | 1.9366 |
| 0.1657 | 20.8333 | 250 | 1.9146 |
| 0.1593 | 22.9167 | 275 | 1.9225 |
| 0.156 | 25.0 | 300 | 1.9411 |
| 0.1549 | 27.0833 | 325 | 1.9504 |
| 0.1525 | 29.1667 | 350 | 1.9608 |
| 0.1511 | 31.25 | 375 | 1.9924 |
| 0.1494 | 33.3333 | 400 | 1.9878 |
| 0.1488 | 35.4167 | 425 | 2.0089 |
| 0.1479 | 37.5 | 450 | 2.0089 |
| 0.1448 | 39.5833 | 475 | 2.0233 |
| 0.1447 | 41.6667 | 500 | 2.0299 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1