README.md

---
library_name: transformers
license: llama3
language:
- ja
- en
tags:
- llama-cpp
---

# Llama-3-ELYZA-JP-8B-GGUF

![Llama-3-ELYZA-JP-8B-image](./key_visual.png)

## Model Description

**Llama-3-ELYZA-JP-8B** is a large language model trained by [ELYZA, Inc](https://elyza.ai/).
Based on [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), it has been enhanced for Japanese usage through additional pre-training and instruction tuning.

For more details, please refer to [our blog post](https://note.com/elyza/n/n360b6084fdbd).

## Quantization

We have prepared two quantized model options, GGUF and AWQ. This is the GGUF (Q4_K_M) model, converted using [llama.cpp](https://github.com/ggerganov/llama.cpp).

The following table shows the performance degradation due to quantization:

| Model | ELYZA-tasks-100 GPT4 score |
| :-------------------------------- | ---: |
| Llama-3-ELYZA-JP-8B               | 3.655 |
| Llama-3-ELYZA-JP-8B-GGUF (Q4_K_M) | 3.57  |
| Llama-3-ELYZA-JP-8B-AWQ           | 3.39  |


## Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux):
```bash
brew install llama.cpp
```

Invoke the llama.cpp server:
```bash
$ llama-server \
--hf-repo elyza/Llama-3-ELYZA-JP-8B-GGUF \
--hf-file Llama-3-ELYZA-JP-8B-q4_k_m.gguf \
--port 8080
```

Call the API using curl:
```bash
$ curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
  "messages": [
    { "role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。" },
    { "role": "user", "content": "古代ギリシャを学ぶ上で知っておくべきポイントは？" }
  ],
  "temperature": 0.6,
  "max_tokens": -1,
  "stream": false
}'
```

Call the API using Python:
```python
import openai

client = openai.OpenAI(
    base_url="http://localhost:8080/v1",
    api_key = "dummy_api_key"
)

completion = client.chat.completions.create(
    model="dummy_model_name",
    messages=[
        {"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、常に日本語で回答してください。"},
        {"role": "user", "content": "古代ギリシャを学ぶ上で知っておくべきポイントは？"}
    ]
)
```

## Use with Desktop App

There are various desktop applications that can handle GGUF models, but here we will introduce how to use the model in the no-code environment [LM Studio](https://lmstudio.ai/).

- **Installation**: Download and install [LM Studio](https://lmstudio.ai/).
- **Downloading the Model**: Search for `elyza/Llama-3-ELYZA-JP-8B-GGUF` in the search bar on the home page 🏠, and download `Llama-3-ELYZA-JP-8B-q4_k_m.gguf`.
- **Start Chatting**: Click on 💬 in the sidebar, select `Llama-3-ELYZA-JP-8B-GGUF` from "Select a Model to load" in the header, and load the model. You can now freely chat with the local LLM.
- **Setting Options**: You can set options from the sidebar on the right. Faster inference can be achieved by setting Quick GPU Offload to Max in the GPU Settings.
- **（For Developers） Starting an API Server**: Click `<->` in the left sidebar and move to the Local Server tab. Select the model and click Start Server to launch an OpenAI API-compatible API server.

## Developers

Listed in alphabetical order.

- [Masato Hirakawa](https://huggingface.co/m-hirakawa)
- [Shintaro Horie](https://huggingface.co/e-mon)
- [Tomoaki Nakamura](https://huggingface.co/tyoyo)
- [Daisuke Oba](https://huggingface.co/daisuk30ba)
- [Sam Passaglia](https://huggingface.co/passaglia)
- [Akira Sasaki](https://huggingface.co/akirasasaki)

## License

[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)

## How to Cite

```tex
@misc{elyzallama2024,
      title={elyza/Llama-3-ELYZA-JP-8B},
      url={https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B},
      author={Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Daisuke Oba and Sam Passaglia and Akira Sasaki},
      year={2024},
}
```

## Citations

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