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dame rajee

damerajee
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reacted to lewtun's post with šŸ”„ about 10 hours ago
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open! šŸ§Ŗ Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1. šŸ§  Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code. šŸ”„ Step 3: show we can go from base model -> SFT -> RL via multi-stage training. Follow along: https://github.com/huggingface/open-r1
reacted to lewtun's post with šŸ¤— about 10 hours ago
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open! šŸ§Ŗ Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1. šŸ§  Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code. šŸ”„ Step 3: show we can go from base model -> SFT -> RL via multi-stage training. Follow along: https://github.com/huggingface/open-r1
reacted to lewtun's post with šŸš€ about 10 hours ago
We are reproducing the full DeepSeek R1 data and training pipeline so everybody can use their recipe. Instead of doing it in secret we can do it together in the open! šŸ§Ŗ Step 1: replicate the R1-Distill models by distilling a high-quality reasoning corpus from DeepSeek-R1. šŸ§  Step 2: replicate the pure RL pipeline that DeepSeek used to create R1-Zero. This will involve curating new, large-scale datasets for math, reasoning, and code. šŸ”„ Step 3: show we can go from base model -> SFT -> RL via multi-stage training. Follow along: https://github.com/huggingface/open-r1
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