Dr. Valerio Rizzo

👀 Just released - 4 new classifier models for curating high-quality data with NVIDIA NeMo Curator ✨ Read blog ➡️ bit.ly/4gLYvQI With these classifier models, you can categorize your data into predefined groups or classes, annotate it with metadata, and optimize your #dataprocessing pipelines. #LLM

🦁 Leo AI and Ollama bring @NVIDIA RTX-accelerated local LLMs to the Brave browser. 🧑‍💻 Summarize webpages, generate content, run models locally, and more. Learn more in this week's #AIDecoded!

Check out how the world’s first #generativeAI models for #OpenUSD development will be available as NVIDIA NIM microservices.

⚡ Llama 3.3 70B from Meta is accelerated by TensorRT-LLM. 🌟 State-of-the-art model on par with Llama 3.1 405B for reasoning, math, instruction following and tool use. 🦙 Explore the preview ➡️

Learn why the NVIDIA Jetson AGX Orin is the leading platform for running transformer models like GPT-J, vision transformers, and stable diffusion at the edge.

Learn why the NVIDIA Jetson AGX Orin is the leading platform for running transformer models like GPT-J, vision transformers, and stable diffusion at the edge.

Check out how the world’s first #generativeAI models for #OpenUSD development will be available as NVIDIA NIM microservices.

#Vista: AI-Focused #Supercomputer in Production for Open Science Community -- https://lnkd.in/dawX_m2s #OpenScience #OpenSource #supercomputers #computation #hardware #AI #ArtificialIntelligence

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 tokens for training new models ✅ Up to 1.8x cost saving for training a family of models 🦙 The effectiveness of these strategies is demonstrated with the Llama 3.1 8B model, which was refined into the Llama-3.1-Minitron 4B. This new model will soon be available in the NVIDIA Hugging Face collection. ➡️ Technical Deep Dive

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 tokens for training new models ✅ Up to 1.8x cost saving for training a family of models 🦙 The effectiveness of these strategies is demonstrated with the Llama 3.1 8B model, which was refined into the Llama-3.1-Minitron 4B. This new model will soon be available in the NVIDIA Hugging Face collection. ➡️ Technical Deep Dive