Instructions to use QuantFactory/gemma-2-baku-2b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/gemma-2-baku-2b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/gemma-2-baku-2b-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/gemma-2-baku-2b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/gemma-2-baku-2b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/gemma-2-baku-2b-GGUF",
	filename="gemma-2-baku-2b.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/gemma-2-baku-2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/gemma-2-baku-2b-GGUF 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 "QuantFactory/gemma-2-baku-2b-GGUF" \
    --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": "QuantFactory/gemma-2-baku-2b-GGUF",
		"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 "QuantFactory/gemma-2-baku-2b-GGUF" \
        --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": "QuantFactory/gemma-2-baku-2b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/gemma-2-baku-2b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/gemma-2-baku-2b-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/gemma-2-baku-2b-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/gemma-2-baku-2b-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/gemma-2-baku-2b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/gemma-2-baku-2b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/gemma-2-baku-2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/gemma-2-baku-2b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-baku-2b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/gemma-2-baku-2b-GGUF

This is quantized version of rinna/gemma-2-baku-2b created using llama.cpp

Original Model Card

`Gemma 2 Baku 2B (rinna/gemma-2-baku-2b)`

Overview

We conduct continual pre-training of google/gemma-2-2b on 80B tokens from a mixture of Japanese and English datasets. The continual pre-training improves the model's performance on Japanese tasks.

The name baku comes from the Japanese word 獏/ばく/Baku, which is a kind of Japanese mythical creature (妖怪/ようかい/Youkai).

Size	Continual Pre-Training	Instruction-Tuning
2B	Gemma 2 Baku 2B [HF]	Gemma 2 Baku 2B Instruct [HF]

Library

The model was trained using code based on Lightning-AI/litgpt.
Model architecture

A 26-layer, 2304-hidden-size transformer-based language model. Please refer to the Gemma 2 Model Card for detailed information on the model's architecture.
Training

The model was initialized with the google/gemma-2-2b model and continually trained on around 80B tokens from a mixture of the following corpora
- Japanese CC-100
- Japanese C4
- Japanese OSCAR
- The Pile
- Wikipedia
- rinna curated Japanese dataset
Contributors

Benchmarking

Please refer to rinna's LM benchmark page.

How to use the model

import transformers
import torch

model_id = "rinna/gemma-2-baku-2b"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16, "attn_implementation": "eager"},
    device_map="auto"
)
output = pipeline(
    "西田幾多郎は、",
    max_new_tokens=256,
    do_sample=True
)
print(output[0]["generated_text"])

It is recommended to use eager attention when conducting batch inference under bfloat16 precision. Currently, Gemma 2 yields NaN values for input sequences with padding when the default attention mechanism (torch.scaled_dot_product_attention) is employed in conjunction with bfloat16.

Tokenization

The model uses the original google/gemma-2-2b tokenizer.

How to cite

@misc{rinna-gemma-2-baku-2b,
    title = {rinna/gemma-2-baku-2b},
    author = {Wakatsuki, Toshiaki and Chen, Xinqi and Sawada, Kei},
    url = {https://huggingface.co/rinna/gemma-2-baku-2b}
}

@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{gemma-2-2024,
    title = {Gemma 2},
    url = {https://www.kaggle.com/models/google/gemma-2},
    publisher = {Kaggle},
    author = {Gemma Team},
    year = {2024}
}

@misc{litgpt-2023,
    author = {Lightning AI},
    title = {LitGPT},
    howpublished = {\url{https://github.com/Lightning-AI/litgpt}},
    year = {2023}
}

License

Gemma Terms of Use

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GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/gemma-2-baku-2b-GGUF

Base model

google/gemma-2-2b

Quantized

(64)

this model

Datasets used to train QuantFactory/gemma-2-baku-2b-GGUF

Paper for QuantFactory/gemma-2-baku-2b-GGUF

Release of Pre-Trained Models for the Japanese Language

Paper • 2404.01657 • Published Apr 2, 2024 • 1