---
library_name: transformers
license: apache-2.0
base_model: cyberagent/Mistral-Nemo-Japanese-Instruct-2408
tags:
- generated_from_trainer
model-index:
- name: outputs/mistral-nemo-webnovels
  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: cyberagent/Mistral-Nemo-Japanese-Instruct-2408
tokenizer_type: AutoTokenizer


load_in_8bit: false
load_in_4bit: false
strict: false


chat_template: chatml
datasets:
  - path: falche/paradox_test_set_200k_sharegpt
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/mistral-nemo-webnovels

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

use_wandb: true
wandb_project: mistral-nemo-webnovels
wandb_entity: augmxnt
wandb_name: mi300x-cyberagent_mistral_nemo_webnovels-fft-dsz3

gradient_accumulation_steps: 1
micro_batch_size: 8
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
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: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# outputs/mistral-nemo-webnovels

This model is a fine-tuned version of [cyberagent/Mistral-Nemo-Japanese-Instruct-2408](https://huggingface.co/cyberagent/Mistral-Nemo-Japanese-Instruct-2408) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6891

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6086        | 0.0008 | 1    | 2.5794          |
| 1.8703        | 0.5    | 615  | 1.8224          |
| 1.7873        | 1.0    | 1230 | 1.7534          |
| 1.6708        | 1.4976 | 1845 | 1.7214          |
| 1.6567        | 1.9976 | 2460 | 1.6919          |
| 1.501         | 2.4951 | 3075 | 1.6984          |
| 1.5237        | 2.9951 | 3690 | 1.6891          |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.0+rocm6.2
- Datasets 3.0.1
- Tokenizers 0.20.1

### Training Infra
Compute sponsored by []HotAisle](https://huggingface.co/hotaisle) on an 8 x MI300X node. See the [WandB Run Logs](https://wandb.ai/augmxnt/mistral-nemo-webnovels) for additional details.