---
license: other
base_model: rinna/llama-3-youko-8b
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft
  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. -->

# sft

This model is a fine-tuned version of [rinna/llama-3-youko-8b](https://huggingface.co/rinna/llama-3-youko-8b) on the data_bricks, the kunishou, the ichikara-004-multi, the ichikara-004-single, the apto_instruct, the apto_dialogue, the oasst_ja and the megagon datasets.
It achieves the following results on the evaluation set:
- Loss: 1.0135

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9155        | 0.1433 | 100  | 1.8934          |
| 1.7826        | 0.2865 | 200  | 1.7808          |
| 1.6897        | 0.4298 | 300  | 1.6719          |
| 1.5887        | 0.5731 | 400  | 1.5738          |
| 1.4628        | 0.7163 | 500  | 1.4660          |
| 1.3751        | 0.8596 | 600  | 1.3671          |
| 1.1263        | 1.0029 | 700  | 1.2831          |
| 0.688         | 1.1461 | 800  | 1.2492          |
| 0.6544        | 1.2894 | 900  | 1.1818          |
| 0.6017        | 1.4327 | 1000 | 1.1207          |
| 0.5763        | 1.5759 | 1100 | 1.0708          |
| 0.5599        | 1.7192 | 1200 | 1.0365          |
| 0.5101        | 1.8625 | 1300 | 1.0170          |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1