---
tags:
- generated_from_trainer
- retnet
model-index:
- name: kakuyomu-retnet-300m-1
  results: []
license: mit
language:
- ja
---

<!-- 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. -->

# LightNovel-Intro-RetNet-400M

This model is a RetNet model trained from scratch using https://github.com/syncdoth/RetNet.

Demo: https://huggingface.co/spaces/isek-ai/LightNovel-Intro-RetNet-400M-Demo

## Usage

First install the required libraries:

```
pip install transformers safetensors timm
```

Example inference script:

```py
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

MODEL_NAME = "isek-ai/LightNovel-Intro-RetNet-400M"

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
).to(device)
gen_config = GenerationConfig.from_pretrained(MODEL_NAME)
gen_config.max_new_tokens = 32

inputs = tokenizer("目が覚めると、", return_tensors="pt", add_special_tokens=False).to(device)

print("Generating...")

result = model.generate(**inputs, generation_config=gen_config)

print(tokenizer.decode(result[0], skip_special_tokens=True))
# 目が覚めると、見知らぬ空間に居た。 「ん......?」 思わずそんな声が出たことに違和感を感じる。確か、気付けば私は
```

## 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: 0.0006
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.5155        | 0.06  | 1000  | 5.5331          |
| 5.0106        | 0.13  | 2000  | 5.1774          |
| 4.793         | 0.19  | 3000  | 4.9399          |
| 4.7078        | 0.26  | 4000  | 4.7737          |
| 4.4789        | 0.32  | 5000  | 4.6373          |
| 4.3269        | 0.38  | 6000  | 4.5422          |
| 4.337         | 0.45  | 7000  | 4.4632          |
| 4.374         | 0.51  | 8000  | 4.4070          |
| 4.1447        | 0.58  | 9000  | 4.3293          |
| 4.1402        | 0.64  | 10000 | 4.2881          |
| 4.1329        | 0.7   | 11000 | 4.2287          |
| 3.9985        | 0.77  | 12000 | 4.1858          |
| 4.1185        | 0.83  | 13000 | 4.1506          |
| 4.0515        | 0.9   | 14000 | 4.0993          |
| 3.9984        | 0.96  | 15000 | 4.0611          |
| 3.7731        | 1.02  | 16000 | 4.0423          |
| 3.7403        | 1.09  | 17000 | 3.8166          |
| 3.6778        | 1.15  | 18000 | 3.8000          |
| 3.7227        | 1.22  | 19000 | 3.7875          |
| 3.6051        | 1.28  | 20000 | 3.7664          |
| 3.6143        | 1.34  | 21000 | 3.7496          |
| 3.6323        | 1.41  | 22000 | 3.7278          |
| 3.6487        | 1.47  | 23000 | 3.7089          |
| 3.6524        | 1.54  | 24000 | 3.6951          |
| 3.5621        | 1.6   | 25000 | 3.6801          |
| 3.5722        | 1.66  | 26000 | 3.6708          |
| 3.5277        | 1.73  | 27000 | 3.6635          |
| 3.6224        | 1.79  | 28000 | 3.6565          |
| 3.5663        | 1.85  | 29000 | 3.6532          |
| 3.5937        | 1.92  | 30000 | 3.6515          |
| 3.5944        | 1.98  | 31000 | 3.6510          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0