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121 lines
3.8 KiB
TeX
121 lines
3.8 KiB
TeX
\section*{Acknowledgments}
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This work stands on the shoulders of countless open-source contributors: PyTorch, Hugging Face Transformers, DeepSpeed, PEFT library developers; conservation biologists collecting field data under harsh conditions; indigenous communities sharing traditional ecological knowledge; blockchain pioneers building censorship-resistant infrastructure; and students worldwide choosing to apply AI for planetary benefit rather than profit maximization. To all who contributed code, data, funding, expertise, or moral support—you are co-authors of this future. Thank you.
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\bibliographystyle{unsrtnat}
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\begin{thebibliography}{99}
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World Wildlife Fund.
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Norwegian Ministry of Agriculture and Food.
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\textit{Qwen2.5: Large Language Models for Diverse Applications}.
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Dettmers, T., et al. (2023).
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\textit{QLoRA: Efficient Finetuning of Quantized LLMs}.
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Ouyang, L., et al. (2022).
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\textit{Training Language Models to Follow Instructions with Human Feedback}.
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NeurIPS 2022.
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Rafailov, R., et al. (2023).
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Dao, T., et al. (2023).
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\textit{FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning}.
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arXiv:2307.08691.
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Unsloth AI. (2024).
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GitHub: github.com/zooai/gym.
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\end{thebibliography}
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