Instructions to use isek-ai/LightNovel-Intro-RetNet-400M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use isek-ai/LightNovel-Intro-RetNet-400M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="isek-ai/LightNovel-Intro-RetNet-400M", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("isek-ai/LightNovel-Intro-RetNet-400M", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use isek-ai/LightNovel-Intro-RetNet-400M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "isek-ai/LightNovel-Intro-RetNet-400M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/LightNovel-Intro-RetNet-400M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/isek-ai/LightNovel-Intro-RetNet-400M

SGLang

How to use isek-ai/LightNovel-Intro-RetNet-400M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "isek-ai/LightNovel-Intro-RetNet-400M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/LightNovel-Intro-RetNet-400M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "isek-ai/LightNovel-Intro-RetNet-400M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "isek-ai/LightNovel-Intro-RetNet-400M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use isek-ai/LightNovel-Intro-RetNet-400M with Docker Model Runner:
```
docker model run hf.co/isek-ai/LightNovel-Intro-RetNet-400M
```

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

Downloads last month: 23

Safetensors

Model size

0.4B params

Tensor type

F32