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  This model was converted to GGUF format from [`Krystalan/DRT-o1-14B`](https://huggingface.co/Krystalan/DRT-o1-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/Krystalan/DRT-o1-14B) for more details on the model.
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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  This model was converted to GGUF format from [`Krystalan/DRT-o1-14B`](https://huggingface.co/Krystalan/DRT-o1-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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  Refer to the [original model card](https://huggingface.co/Krystalan/DRT-o1-14B) for more details on the model.
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+ ---
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+ Model details:
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+ -
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+
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+ In this work, we introduce DRT-o1, an attempt to bring the success of
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+ long thought reasoning to neural machine translation (MT). To this end,
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+
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+
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+ 🌟 We mine English sentences with similes or metaphors from existing
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+ literature books, which are suitable for translation via long thought.
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+ 🌟 We propose a designed multi-agent framework with three agents
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+ (i.e., a translator, an advisor and an evaluator) to synthesize the MT
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+ samples with long thought. There are 22,264 synthesized samples in
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+ total.
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+ 🌟 We train DRT-o1-8B, DRT-o1-7B and DRT-o1-14B using
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+ Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct as
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+ backbones.
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+
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+
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+
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+
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+ Our goal is not to achieve competitive performance with OpenAI’s O1
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+ in neural machine translation (MT). Instead, we explore technical routes
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+ to bring the success of long thought to MT. To this end, we introduce
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+ DRT-o1, a byproduct of our exploration, and we hope it could facilitate the corresponding research in this direction.
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+
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+ ---
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  ## Use with llama.cpp
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  Install llama.cpp through brew (works on Mac and Linux)
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