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library_name: transformers
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---
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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[
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###
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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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
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![Llama-3-ELYZA-JP-8B-image](./key_visual.png)
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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.
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For more details, please refer to [our blog post](https://note.com/elyza/n/n360b6084fdbd).
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### Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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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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text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
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model_name = "elyza/Llama-3-ELYZA-JP-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto",
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)
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model.eval()
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messages = [
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{"role": "system", "content": DEFAULT_SYSTEM_PROMPT},
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{"role": "user", "content": text},
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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token_ids = tokenizer.encode(
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prompt, add_special_tokens=False, return_tensors="pt"
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)
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with torch.no_grad():
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output_ids = model.generate(
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token_ids.to(model.device),
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max_new_tokens=1200,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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)
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output = tokenizer.decode(
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output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
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)
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print(output)
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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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