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In a Training Loop 🔄
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Asankhaya Sharma PRO

codelion
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http://asankhaya.github.io/

AI & ML interests

Creator of OptiLLM, OpenEvolve, Adaptive Classifier, and Ellora. Pioneering a new category in AI infrastructure: inference-time compute for LLMs.

Recent Activity

reacted to their post with 🚀 3 days ago
Recently, Essential AI released a new 8B base model https://huggingface.co/EssentialAI/rnj-1 they highlighted the importance of data mix for pretraning - "In the long run, we expect our methods to automatically represent, transform, and blend data to optimize measurable abilities in pre-training. Our work on modeling data taxonomies led to new approaches for jointly clustering and mixing data distributions under data repetition penalties. Many improvements in our STEM abilities can be traced back to this. " This resonates with the recent work we did around optimal dataset mixing for pretraining where we saw have the right mix can increase the efficiency of training - https://huggingface.co/blog/codelion/optimal-dataset-mixing
reacted to their post with 👍 3 days ago
Recently, Essential AI released a new 8B base model https://huggingface.co/EssentialAI/rnj-1 they highlighted the importance of data mix for pretraning - "In the long run, we expect our methods to automatically represent, transform, and blend data to optimize measurable abilities in pre-training. Our work on modeling data taxonomies led to new approaches for jointly clustering and mixing data distributions under data repetition penalties. Many improvements in our STEM abilities can be traced back to this. " This resonates with the recent work we did around optimal dataset mixing for pretraining where we saw have the right mix can increase the efficiency of training - https://huggingface.co/blog/codelion/optimal-dataset-mixing
reacted to their post with 🔥 3 days ago
Recently, Essential AI released a new 8B base model https://huggingface.co/EssentialAI/rnj-1 they highlighted the importance of data mix for pretraning - "In the long run, we expect our methods to automatically represent, transform, and blend data to optimize measurable abilities in pre-training. Our work on modeling data taxonomies led to new approaches for jointly clustering and mixing data distributions under data repetition penalties. Many improvements in our STEM abilities can be traced back to this. " This resonates with the recent work we did around optimal dataset mixing for pretraining where we saw have the right mix can increase the efficiency of training - https://huggingface.co/blog/codelion/optimal-dataset-mixing
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