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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 in the same directory as the inference script:

git clone https://github.com/syncdoth/RetNet.git

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

Framework versions

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