54 lines
2.3 KiB
Markdown
54 lines
2.3 KiB
Markdown
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---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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base_model:
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- Qwen/Qwen2-1.5B-Instruct
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datasets:
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- HuggingFaceTB/smollm-corpus
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---
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# 🐟 TAID-LLM-1.5B
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🤗 [Models](https://huggingface.co/SakanaAI) | 📚 [Paper](https://arxiv.org/abs/2501.16937) | 📝 [Blog](https://sakana.ai/taid/) | 🐦 [Twitter](https://twitter.com/SakanaAILabs)
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**TAID-LLM-1.5B** is an English language model created through TAID (Temporally Adaptive Interpolated Distillation), our new knowledge distillation method.
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We used [Qwen2-72B-Instruct](https://huggingface.co/Qwen/Qwen2-72B-Instruct) as the teacher model and [Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) as the student model.
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## Model Details
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- **Developed by:** [Sakana AI](https://sakana.ai/)
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- **Model type:** Autoregressive Language Model
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- **Language(s):** English
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- **License:** [Apache License, Version 2.0](./LICENSE)
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- **Paper:** https://arxiv.org/abs/2501.16937
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- **Blog:** https://sakana.ai/taid/
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## Uses
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This model is provided for research and development purposes only and should be considered as an experimental prototype.
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It is not intended for commercial use or deployment in mission-critical environments.
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Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed.
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Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained.
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Users must fully understand the risks associated with the use of this model and use it at their own discretion.
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## Acknowledgement
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We would like to thank the developers of the source models for their contributions and for making their work available.
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This model is based on results obtained from a project, JPNP20017, subsidized by the New Energy and Industrial Technology Development Organization (NEDO).
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## Citation
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```bibtex
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@misc{sakana2025taid,
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title = {TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models},
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author. = {Makoto Shing and Kou Misaki and Han Bao and Sho Yokoi and Takuya Akiba},
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year = {2025},
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eprint = {2501.16937},
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archivePrefix = {arXiv},
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primaryClass = {cs.LG},
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url = {https://arxiv.org/abs/2501.16937}
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}
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```
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