---
library_name: transformers
license: llama3.1
base_model: anastas5/llama3.1-8B-Instruct-rus-test-v2
tags:
- generated_from_trainer
model-index:
- name: home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: anastas5/llama3.1-8B-Instruct-rus-test-v2

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: anastas5/dataset-rus-test-six
    type: sharegpt
    conversation: llama-3
dataset_prepared_path: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/prepared_tagengo_rus
val_set_size: 0.05
output_dir: /home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

use_wandb: false

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# home/ubuntu/llm_training/axolotl/llama3-8b-gpt-4o-ru/output_llama3_8b_gpt_4o_ru

This model is a fine-tuned version of [anastas5/llama3.1-8B-Instruct-rus-test-v2](https://huggingface.co/anastas5/llama3.1-8B-Instruct-rus-test-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7729

## 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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.957         | 0.0769 | 1    | 0.8838          |
| 0.9398        | 0.2308 | 3    | 0.8762          |
| 1.0969        | 0.4615 | 6    | 0.8373          |
| 0.9608        | 0.6923 | 9    | 0.8226          |
| 0.8364        | 0.9231 | 12   | 0.8146          |
| 0.7566        | 1.1154 | 15   | 0.7914          |
| 0.7927        | 1.3462 | 18   | 0.7818          |
| 0.74          | 1.5769 | 21   | 0.7784          |
| 0.7247        | 1.8077 | 24   | 0.7783          |
| 0.7261        | 2.0385 | 27   | 0.7748          |
| 0.7255        | 2.2308 | 30   | 0.7727          |
| 0.6439        | 2.4615 | 33   | 0.7726          |
| 0.5354        | 2.6923 | 36   | 0.7725          |
| 0.638         | 2.9231 | 39   | 0.7734          |
| 0.6246        | 3.0769 | 42   | 0.7726          |
| 0.5374        | 3.3077 | 45   | 0.7726          |
| 0.5539        | 3.5385 | 48   | 0.7729          |
| 0.6056        | 3.7692 | 51   | 0.7729          |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1