---
license: other
base_model: lightblue/suzume-llama-3-8B-multilingual
tags:
- generated_from_trainer
model-index:
- name: workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: lightblue/suzume-llama-3-8B-multilingual
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer  # PreTrainedTokenizerFast

load_in_8bit: false
load_in_4bit: false
strict: false

rl: orpo
orpo_alpha: 0.1
remove_unused_columns: false

chat_template: chatml
datasets:
  - path: lightblue/mitsu_full_borda
    type: orpo.chat_template
    conversation: llama-3
dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual-orpo/prepared_mitsu_full_borda
val_set_size: 0.02
output_dir: /workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: true

use_wandb: true
wandb_project: axolotl
wandb_entity: peterd
wandb_name: mitsu_full_borda

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 8e-6

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: 20
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>
```

</details><br>

# workspace/llm_training/axolotl/llama3-multilingual-orpo/output_mitsu_full_borda

This model is a fine-tuned version of [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1005

## 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: 8e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.6836        | 0.01  | 1    | 7.6268          |
| 3.9429        | 0.05  | 4    | 2.3993          |
| 0.1193        | 0.1   | 8    | 0.1331          |
| 0.1039        | 0.15  | 12   | 0.1209          |
| 0.1082        | 0.2   | 16   | 0.1189          |
| 0.1209        | 0.25  | 20   | 0.1180          |
| 0.1106        | 0.3   | 24   | 0.1157          |
| 0.103         | 0.35  | 28   | 0.1156          |
| 0.1141        | 0.41  | 32   | 0.1123          |
| 0.1156        | 0.46  | 36   | 0.1104          |
| 0.0925        | 0.51  | 40   | 0.1089          |
| 0.1113        | 0.56  | 44   | 0.1052          |
| 0.1146        | 0.61  | 48   | 0.1073          |
| 0.1029        | 0.66  | 52   | 0.1086          |
| 0.1198        | 0.71  | 56   | 0.1072          |
| 0.1205        | 0.76  | 60   | 0.1062          |
| 0.1209        | 0.81  | 64   | 0.1041          |
| 0.1047        | 0.86  | 68   | 0.1020          |
| 0.0798        | 0.91  | 72   | 0.1008          |
| 0.1007        | 0.96  | 76   | 0.1005          |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0