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---
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
```
Then clone the repository of [implementation of RetNet written by syncdoth](https://github.com/syncdoth/RetNet) in the same directory as the inference script:
```
git clone https://github.com/syncdoth/RetNet.git
```
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("็ฎใ่ฆใใใจใ", 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))
# ็ฎใ่ฆใใใจใ่ฆ็ฅใใฌ็ฉบ้ใซๅฑ
ใใ ใใ......?ใ ๆใใใใใชๅฃฐใๅบใใใจใซ้ๅๆใๆใใใ็ขบใใๆฐไปใใฐ็งใฏ
```
## 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 |