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README.md
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This model was converted to GGUF format from [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-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/deepseek-ai/DeepSeek-R1-Distill-Qwen-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 [`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-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/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) for more details on the model.
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Model details:
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We introduce our first-generation reasoning models, DeepSeek-R1-Zero
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and DeepSeek-R1.
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DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning
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(RL) without supervised fine-tuning (SFT) as a preliminary step,
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demonstrated remarkable performance on reasoning.
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With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and
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interesting reasoning behaviors.
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However, DeepSeek-R1-Zero encounters challenges such as endless
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repetition, poor readability, and language mixing. To address these
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issues and further enhance reasoning performance,
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we introduce DeepSeek-R1, which incorporates cold-start data before RL.
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DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math,
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code, and reasoning tasks.
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To support the research community, we have open-sourced
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DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from
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DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B
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outperforms OpenAI-o1-mini across various benchmarks, achieving new
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state-of-the-art results for dense models.
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NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.
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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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