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:

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