| Model Type | |
| Use Cases |
| Areas: | | research, commercial applications |
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| Applications: | | instruction-following conversational agent |
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| Primary Use Cases: | | Bilingual text generation |
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| Limitations: | | Sensitive to decoding hyper-parameters. |
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| Considerations: | | Decoding hyper-parameters should be carefully chosen. |
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| Additional Notes | | The model uses a sentencepiece-based tokenizer with a vocabulary size of 65,536. |
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| Supported Languages | | ja (full proficiency), en (full proficiency) |
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| Training Details |
| Data Sources: | |
| Methodology: | | Supervised Fine-Tuning (SFT) and PPO-based Reinforcement Learning (RL) |
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| Model Architecture: | | 36-layer, 2816-hidden-size transformer-based language model |
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| Input Output |
| Input Format: | | A special format for conversation between 'γ¦γΌγΆγΌ' and 'γ·γΉγγ ', ending with 'γ·γΉγγ : '. |
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| Accepted Modalities: | |
| Output Format: | | Textual response in the set language (Japanese/English) |
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| Performance Tips: | | Adjust decoding hyper-parameters for optimal performance. |
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