Instructions to use thefrigidliquidation/pythia-1b-lightnovels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thefrigidliquidation/pythia-1b-lightnovels with Transformers:

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

pipe = pipeline("text-generation", model="thefrigidliquidation/pythia-1b-lightnovels")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thefrigidliquidation/pythia-1b-lightnovels")
model = AutoModelForCausalLM.from_pretrained("thefrigidliquidation/pythia-1b-lightnovels")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use thefrigidliquidation/pythia-1b-lightnovels with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thefrigidliquidation/pythia-1b-lightnovels"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thefrigidliquidation/pythia-1b-lightnovels",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/thefrigidliquidation/pythia-1b-lightnovels

SGLang

How to use thefrigidliquidation/pythia-1b-lightnovels 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 "thefrigidliquidation/pythia-1b-lightnovels" \
    --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": "thefrigidliquidation/pythia-1b-lightnovels",
		"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 "thefrigidliquidation/pythia-1b-lightnovels" \
        --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": "thefrigidliquidation/pythia-1b-lightnovels",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use thefrigidliquidation/pythia-1b-lightnovels with Docker Model Runner:
```
docker model run hf.co/thefrigidliquidation/pythia-1b-lightnovels
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Pythia 1B fine-tuned on Light Novels

This model was fine-tuned on light and web novels. This model was trained for translation, but can do generation too.

This model is a test of using monolingual data to improve translation as well as improving translation by adding similar sentence pairs to prompts.

English generation

To generate English text with this model, start your prompt with <|gen_en|>.

Japanese generation

To generate Japanese text with this model, start your prompt with <|gen_ja|>.

Japanese to English translation

To translate, format your prompt as such

<|tl_ja|>JAPANESE EXAMPLE SENTENCE 1<|tl_en|>ENGLISH EXAMPLE SENTENCE 1<|tl_end|>
<|tl_ja|>JAPANESE EXAMPLE SENTENCE 2<|tl_en|>ENGLISH EXAMPLE SENTENCE 2<|tl_end|>
<|tl_ja|>JAPANESE SENTENCE TO TRANSLATE<|tl_en|>

Downloads last month: 10

Safetensors

Model size

1B params

Tensor type

F32

Model tree for thefrigidliquidation/pythia-1b-lightnovels

Quantizations

2 models