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

Libraries

How to use QuantFactory/llama-3-youko-8b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/llama-3-youko-8b-GGUF",
	filename="llama-3-youko-8b.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/llama-3-youko-8b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/llama-3-youko-8b-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Ollama
How to use QuantFactory/llama-3-youko-8b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/llama-3-youko-8b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/llama-3-youko-8b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/llama-3-youko-8b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/llama-3-youko-8b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/llama-3-youko-8b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/llama-3-youko-8b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/llama-3-youko-8b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-youko-8b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/llama-3-youko-8b-GGUF

This is quantized version of rinna/llama-3-youko-8b created using llama.cpp

Model Description

Overview

We conduct continual pre-training of meta-llama/Meta-Llama-3-8B on 22B tokens from a mixture of Japanese and English datasets. The continual pre-training significantly improves the model's performance on Japanese tasks.

The name youko comes from the Japanese word 妖狐/ようこ/Youko, which is a kind of Japanese mythical creature (妖怪/ようかい/Youkai).

Library

The model was trained using code based on EleutherAI/gpt-neox.
Model architecture

A 32-layer, 4096-hidden-size transformer-based language model. Refer to the Llama 3 Model Card for architecture details.
Training: Built with Meta Llama 3

The model was initialized with the meta-llama/Meta-Llama-3-8B model and continually trained on around 22B tokens from a mixture of the following corpora
- Japanese CC-100
- Japanese C4
- Japanese OSCAR
- The Pile
- Wikipedia
- rinna curated Japanese dataset
Contributors
- Koh Mitsuda
- Kei Sawada

Benchmarking

Please refer to rinna's LM benchmark page.

Tokenization

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

How to cite original model

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

@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},
    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}
}

@software{gpt-neox-library,
    title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}},
    author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
    doi = {10.5281/zenodo.5879544},
    month = {8},
    year = {2021},
    version = {0.0.1},
    url = {https://www.github.com/eleutherai/gpt-neox},
}

License

Meta Llama 3 Community License

Downloads last month: 278

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/llama-3-youko-8b-GGUF

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

rinna/llama-3-youko-8b

Quantized

(9)

this model

Datasets used to train QuantFactory/llama-3-youko-8b-GGUF

Paper for QuantFactory/llama-3-youko-8b-GGUF

Release of Pre-Trained Models for the Japanese Language

Paper • 2404.01657 • Published Apr 2, 2024 • 1