Saurav Muralidharan @srv_m
Research Scientist @NVIDIA | Making LLMs More Efficient sauravm.com Joined March 2008-
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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…
We are hiring researchers working in LLM and VLM efficiency! Applications are open for PhD students graduating in 2025; and senior researchers with PhD. Check requirements for the position. Apply here: nvidia.wd5.myworkdayjobs.com/NVIDIAExternal… Senior researchers: nvidia.wd5.myworkdayjobs.com/NVIDIAExternal……
🚀 @NeurIPSConf Spotlight! 🥳 Imagine fine-tuning an LLM with just a sparsity mask! In our latest work, we freeze the LLM and use 2:4 structured sparsity to learn binary masks for each linear layer. Thanks to NVIDIA Ampere’s 2:4 sparsity, we can achieve up to 2x compute…
👀 Experience high-efficiency NVIDIA Llama-3.1-Nemotron-51B - a NAS-optimized model achieving 2x throughput while preserving accuracy runs on a single H100 GPU. ✨Try out the Llama-3.1-Nemotron-51B NIM through the API from ai.nvidia.com or download from @huggingface.…
Introducing NVLM 1.0, a family of frontier-class multimodal LLMs that achieve state-of-the-art results on vision-language tasks, rivaling the leading proprietary models (e.g., GPT-4o) and open-access models (e.g., InternVL 2). Remarkably, NVLM 1.0 shows improved text-only…
LLM Pruning and Distillation in Practice: The Minitron Approach abs: arxiv.org/abs/2408.11796 models: huggingface.co/nvidia/Mistral… huggingface.co/nvidia/Llama-3… huggingface.co/nvidia/Llama-3… Compressing Llama 3.1 8B and Mistral NeMo 12B to 4B and 8B, respectively, with teacher correction,…
Today we released Mistral-NeMo-Minitron 8B, a pruned and distilled version of the open @MistralAI NeMo 12B model, achieving high accuracy across nine popular benchmarks for chatbots, virtual assistants, content generation, coding, and educational tools. ➡️…
🌟 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…
See how our #NVIDIAResearch team has developed a method to efficiently create smaller, accurate language models by using structured weight pruning and knowledge distillation - offering several advantages for developers: ✅ 16% better performance on MMLU scores ✅ 40x fewer…
🚀 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…
🤖 Excited to announce Minitron, a new family of language models obtained through a combination of weight pruning and knowledge distillation! Our models are available on HF with a permissive license. Give them a try today!
🤖 Excited to announce Minitron, a new family of language models obtained through a combination of weight pruning and knowledge distillation! Our models are available on HF with a permissive license. Give them a try today!
@MistralAI and @nvidia announce Mistral-NeMo 12B, an awesome bite-size model released under Apache 2.0 that we jointly trained. FP8 aligned checkpoint and 128k context window, great benchmark scores. blogs.nvidia.com/blog/mistral-n… mistral.ai/news/mistral-n…
🚀 Introducing Flextron - a Many-in-One LLM - Oral at ICML! Train one model and get many optimal models for each GPU at inference without any additional retraining. 🌟 🔗 Paper: arxiv.org/abs/2406.10260 Main benefits with only 5% post-training finetuning: ✅ Best model for…
We are now having full conversations with Figure 01, thanks to our partnership with OpenAI. Our robot can: - describe its visual experience - plan future actions - reflect on its memory - explain its reasoning verbally Technical deep-dive 🧵:
Fun story from our internal testing on Claude 3 Opus. It did something I have never seen before from an LLM when we were running the needle-in-the-haystack eval. For background, this tests a model’s recall ability by inserting a target sentence (the "needle") into a corpus of…
here is sora, our video generation model: openai.com/sora today we are starting red-teaming and offering access to a limited number of creators. @_tim_brooks @billpeeb @model_mechanic are really incredible; amazing work by them and the team. remarkable moment.
Announcing FlashAttention-2! We released FlashAttention a year ago, making attn 2-4 faster and is now widely used in most LLM libraries. Recently I’ve been working on the next version: 2x faster than v1, 5-9x vs standard attn, reaching 225 TFLOPs/s training speed on A100. 1/
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