sharathts @sharath_ts
Deep Learning Research @Nvidia United States Joined December 2009-
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NVIDIA has released Nemotron Nano 9B V2, a small 9B reasoning model that scores 43 on the Artificial Analysis Intelligence Index, the highest yet for <10B models Nemotron 9B V2 is the first Nemotron model pre-trained by @nvidia. Previous Nemotron models have been developed by…
📢New efficient Hybrid-SLM from NVIDIA-Nemotron-Nano-v2-9B: ❗️6x faster than Qwen3-8B because of Hybrid (Mamba2+Attention) design. We tried something new: pretrain & align a 12B reasoning model → compress to 9B. First real stab at reasoning-model compression. Key takeaways…
Sharing our team’s latest work on Hymba - an efficient small language model with hybrid architecture. Tech report: arxiv.org/abs/2411.13676 Discover the tradeoff between Mamba and Attention, how they can be combined, how attention sink and forced-to-attend phenomena can be…
📣 Announcing Mistral-NeMo-Minitron 8B Instruct, one of the most advanced models in its size, delivering higher accuracy and lower computational cost, with leading performance on six important benchmarks. Technical deep dive ➡️ developer.nvidia.com/blog/mistral-n… This powerful model was…
Excited to introduce MN-Minitron-8B-Instruct 📗! We've developed an even more powerful instruct model than its parent, Mistral-NeMo-12B, with significant improvements over LLaMa3.1-8B-Instruct as well! Weights on HF: huggingface.co/nvidia/Mistral… Demo: build.nvidia.com/nvidia/mistral… Our…
LLaMa-3.2 models have been released, featuring smaller sizes of 1B and 3B parameters. These models are derived from an 8B parent model through pruning and distillation, using the same pipeline as Minitron, which we proposed this summer and it was accepted for NeurIPS! 😃 Pruning…
Amazing effort by the MagpieLM team! 👏 They showed that our pruned LLaMa-3.1-4B can even outperform larger 8B models! 🚀 Nice to see the community adapting our models—this encourages us to release more! 🎉💪
Amazing effort by the MagpieLM team! 👏 They showed that our pruned LLaMa-3.1-4B can even outperform larger 8B models! 🚀 Nice to see the community adapting our models—this encourages us to release more! 🎉💪
🌟 The best 8B Base model via pruning and distillation! 🚀 Introducing Mistral-NeMo-Minitron-8B-Base model we derived from the recent Mistral-NeMo-12B. Our recipe: finetune teacher on 100B tokens, prune to 8B params, run teacher-student distillation on <400B tokens. Result: the…
🚀 We've pruned LLaMa3.1 down to 4B parameters, delivering a smaller and more efficient model! Based on our recent paper: arxiv.org/abs/2407.14679 📖 Learn all about it in our blog: developer.nvidia.com/blog/how-to-pr… 🔗 META's announcement: ai.meta.com/blog/nvidia-ll… 👐 Checkpoints at HF this…
🚀 40x Faster Model Training via Pruning and Distillation! Permissive Minitron-4B and Minitron-8B models! 🔗 Paper: arxiv.org/abs/2407.14679 🔗 GitHub: github.com/NVlabs/Minitron 🔗 Models on HF: bit.ly/4ffjnQj Key highlights of 4B/8B models: 📊 2.6B/6.2B active…
Markus Kliegl @MarkusKliegl
92 Followers 123 Following Research scientist at NVIDIA | Previously deep learning at Apple, DeepMind, Baidu Research
Kezhi Kong @KezhiKong
349 Followers 377 Following Research Scientist @NVIDIA working on Nemotron-*; PhD @UMDCS; BS @ZJU_China; opinions are my own
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