Instructions to use rinna/llama-3-youko-8b-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rinna/llama-3-youko-8b-gptq with Transformers:

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

pipe = pipeline("text-generation", model="rinna/llama-3-youko-8b-gptq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/llama-3-youko-8b-gptq")
model = AutoModelForCausalLM.from_pretrained("rinna/llama-3-youko-8b-gptq", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rinna/llama-3-youko-8b-gptq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rinna/llama-3-youko-8b-gptq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rinna/llama-3-youko-8b-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rinna/llama-3-youko-8b-gptq

SGLang

How to use rinna/llama-3-youko-8b-gptq 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 "rinna/llama-3-youko-8b-gptq" \
    --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": "rinna/llama-3-youko-8b-gptq",
		"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 "rinna/llama-3-youko-8b-gptq" \
        --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": "rinna/llama-3-youko-8b-gptq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rinna/llama-3-youko-8b-gptq with Docker Model Runner:
```
docker model run hf.co/rinna/llama-3-youko-8b-gptq
```

`Llama 3 Youko 8B GPTQ (rinna/llama-3-youko-8b-gptq)`

Overview

rinna/llama-3-youko-8b-gptq is the quantized model for rinna/llama-3-youko-8b using AutoGPTQ. The quantized version is 4x smaller than the original model and thus requires less memory and provides faster inference.

Size	Continual Pre-Training	Instruction-Tuning
8B	Llama 3 Youko 8B [HF] [GPTQ]	Llama 3 Youko 8B Instruct [HF] [GPTQ]
70B	Llama 3 Youko 70B [HF] [GPTQ]	Llama 3 Youko 70B Instruct [HF] [GPTQ]

Training: Built with Meta Llama 3

See rinna/llama-3-youko-8b for details about model architecture and data.
Contributors
Release date

July 25, 2024

Benchmarking

Please refer to rinna's LM benchmark page (Sheet 20240725).

How to use the model

import transformers
import torch

model_id = "rinna/llama-3-youko-8b-gptq"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    device_map="auto"
)
output = pipeline(
    "西田幾多郎は、",
    max_new_tokens=256,
    do_sample=True
)
print(output[0]["generated_text"])

Tokenization

The model uses the original meta-llama/Meta-Llama-3-8B tokenizer.

How to cite

@misc{rinna-llama-3-youko-8b-gptq,
    title = {rinna/llama-3-youko-8b-gptq},
    author = {Wakatsuki, Toshiaki and Mitsuda, Koh and Chen, Xinqi and Sawada, Kei},
    url = {https://huggingface.co/rinna/llama-3-youko-8b-gptq}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}

References

@article{llama3modelcard,
    title = {Llama 3 Model Card},
    author = {AI@Meta},
    year = {2024},
    url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}

@article{frantar2022gptq,
    title = {{GPTQ}: Accurate Post-training Compression for Generative Pretrained Transformers},
    author = {Frantar, Elias and Ashkboos, Saleh and Hoefler, Torsten and Alistarh, Dan},
    year = {2022},
    url = {https://arxiv.org/abs/2210.17323}
}