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Mohamed Rashad PRO
MohamedRashad
AI & ML interests
Computer Vision, Robotics, Natural Language Processing
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The winners of Best Paper Award in NeurIPs2024 (FoundationVision) https://huggingface.co./papers/2404.02905 has just released a new paper called infinty:
https://huggingface.co./papers/2412.04431
And i managed to build a space for it so anyone can try it out: https://huggingface.co./spaces/MohamedRashad/Infinity
The idea of a text to image model using autoregressive archticture is quite interesting in my opinion.
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3C3H AraGen Leaderboard welcomes today https://huggingface.co./deepseek-ai/DeepSeek-V3 and 12 other models (including the late gpt-3.5 💀) to the ranking of best LLMs in Arabic !
Observations:
- DeepSeek-v3 ranked 3rd and only Open model among the top 5 !
- A 14B open model (https://huggingface.co./Qwen/Qwen2.5-14B-Instruct) outperforms gpt-3.5-turbo-0125 (from last year). This shows how much we came in advancing and supporting Arabic presence within the LLM ecosystem !
- Contrary to what observed in likelihood-acc leaderboards (like https://huggingface.co./spaces/OALL/Open-Arabic-LLM-Leaderboard) further finetuned models like https://huggingface.co./maldv/Qwentile2.5-32B-Instruct actually decreased the performance compared to the original model https://huggingface.co./Qwen/Qwen2.5-32B-Instruct.
It's worth to note that the decrease is statiscally insignificant which imply that at best, the out-domain finetuning do not really hurts the model original capabilities acquired during pretraining.
Previous work addressed this (finetuning VS pretraining) but more investigation in this regard is required (any PhDs here ? This could be your question ...)
Check out the latest rankings: https://huggingface.co./spaces/inceptionai/AraGen-Leaderboard
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