157 lines
4.6 KiB
Markdown
157 lines
4.6 KiB
Markdown
---
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library_name: transformers
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license: llama3
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language:
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- ja
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- en
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---
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# Llama-3-ELYZA-JP-8B-AWQ
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## Model Description
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**Llama-3-ELYZA-JP-8B** is a large language model trained by [ELYZA, Inc](https://elyza.ai/).
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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. (Built with Meta Llama3)
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For more details, please refer to [our blog post](https://note.com/elyza/n/n360b6084fdbd).
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## Quantization
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We have prepared two quantized model options, GGUF and AWQ. This is the [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) model.
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The following table shows the performance degradation due to quantization:
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| Model | ELYZA-tasks-100 GPT4 score |
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| :-------------------------------- | ---: |
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| [Llama-3-ELYZA-JP-8B](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B) | 3.655 |
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| [Llama-3-ELYZA-JP-8B-GGUF (Q4_K_M)](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF) | 3.57 |
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| [Llama-3-ELYZA-JP-8B-AWQ](https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-AWQ) | 3.39 |
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## Use with vLLM
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Install vLLM:
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```bash
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pip install vllm
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```
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### vLLM Offline Batched Inference
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```python
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from vllm import LLM, SamplingParams
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llm = LLM(model="elyza/Llama-3-ELYZA-JP-8B-AWQ", quantization="awq")
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tokenizer = llm.get_tokenizer()
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DEFAULT_SYSTEM_PROMPT = "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。"
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sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=1000)
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messages_batch = [
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[
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"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？"}
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],
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[
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"role": "user", "content": "クマが海辺に行ってアザラシと友達になり、最終的には家に帰るというプロットの短編小説を書いてください。"}
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]
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]
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prompts = [
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tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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for messages in messages_batch
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]
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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for output in outputs:
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print(output.outputs[0].text)
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print("=" * 50)
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```
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### vLLM OpenAI Compatible Server
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Start the API server:
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```bash
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python -m vllm.entrypoints.openai.api_server \
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--model elyza/Llama-3-ELYZA-JP-8B-AWQ \
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--port 8000 \
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--host localhost \
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--quantization awq
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```
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Call the API using curl:
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```bash
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "elyza/Llama-3-ELYZA-JP-8B-AWQ",
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"messages": [
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{ "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。" },
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{ "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？" }
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],
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"temperature": 0.6,
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"max_tokens": 1000,
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"stream": false
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}'
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```
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Call the API using Python:
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```python
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import openai
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client = openai.OpenAI(
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base_url="http://localhost:8000/v1",
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api_key = "dummy_api_key"
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)
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completion = client.chat.completions.create(
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model="elyza/Llama-3-ELYZA-JP-8B-AWQ",
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messages=[
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{"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。"},
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{"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？"}
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]
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)
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```
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## Developers
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Listed in alphabetical order.
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- [Masato Hirakawa](https://huggingface.co/m-hirakawa)
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- [Shintaro Horie](https://huggingface.co/e-mon)
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- [Tomoaki Nakamura](https://huggingface.co/tyoyo)
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- [Daisuke Oba](https://huggingface.co/daisuk30ba)
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- [Sam Passaglia](https://huggingface.co/passaglia)
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- [Akira Sasaki](https://huggingface.co/akirasasaki)
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## License
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[Meta Llama 3 Community License](https://llama.meta.com/llama3/license/)
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## How to Cite
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```tex
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@misc{elyzallama2024,
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title={elyza/Llama-3-ELYZA-JP-8B},
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url={https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B},
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author={Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Daisuke Oba and Sam Passaglia and Akira Sasaki},
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year={2024},
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}
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```
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## Citations
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```tex
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@article{llama3modelcard,
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title={Llama 3 Model Card},
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author={AI@Meta},
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year={2024},
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url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
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}
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```
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