Ronnie Green’s Post

Mistral and Nvidia Unveil Small AI Powerhouse Mistral AI and Nvidia have introduced Mistral NeMo, a new 12B parameter small language model that outperforms competitors like Gemma 2 9B and Llama 3 8B on key benchmarks, with an impressive context window increase. Key Highlights: Expanded Context: 128k token context window, providing SOTA performance in reasoning, world knowledge, and coding accuracy. Versatility: Excels in multi-turn conversations, math, and common sense reasoning. Efficient Tokenizer: ‘Tekken’ tokenizer allows for 30% more content within the context window across 100+ languages. Hardware Compatibility: Runs on single NVIDIA L40S, GeForce RTX 4090, or RTX 4500 GPU. This marks a shift toward powerful, compact AI models, enhancing capabilities without compromising speed and size. #AI #Nvidia #MistralAI #TechInnovation #SmallModels #OpenSource https://lnkd.in/dxryesgD?

🔥 Size Matters... But So Does Power! Ever heard of a compact AI model that can still pack a punch? 🤔 Get ready to be amazed by Llama-3.1-Minitron-4B-Width-Base! 🤯 NVIDIA has created this compact LLM that's perfect for various text generation tasks, even with its smaller size. In this carousel, we'll unravel the secrets behind this AI marvel: - Uncover its unique training and architecture. 🧠 - Explore its potential applications. 🚀 - Learn how to harness its power. 💪 Ready to dive in? Check out the carousel below! 👇 Want to explore further? Model: https://lnkd.in/gvRYiDEN GGUF Set: https://lnkd.in/gj2Ud4Ky Arxiv: https://lnkd.in/gaUM3pGr ⚠️ Note: Hugging Face Transformers support is under consideration. For now, follow the developer instructions here or use NeMo v.24.05. You can also find unofficial quantized GGUF versions here 📌 Licensing: NVIDIA Open Model License. #AI #Llama #CompactModels #TextGeneration #NVIDIA [Insert link to your LinkedIn carousel post here]

Learn more about how NVIDIA CUDA-X data processing libraries will be integrated with HP AI workstation solutions to turbocharge the data preparation and processing work that forms the foundation of #generativeAI development. #HPAmplify

Microsoft launched their Phi-3 LLMs, surpassing the performance of the larger Llama 3 8B with a model less than half its size. The Phi-3-mini, with only 3.8 billion parameters and trained on significantly fewer data tokens, achieves this by focusing on high-quality, heavily filtered datasets. Despite its smaller size, it's powerful enough to rival larger models and can even run on mobile devices, needing just 1.8GB of memory when optimized. Phi-3 also maintains compatibility using the same tokenizer as the previous Llama 2.

Our recent #pruning and #distillation method for #LLMs in action! Read in a blogpost: How to Prune and Distill #Llama-3.1 8B to an NVIDIA Llama-3.1-#Minitron 4B Model (https://lnkd.in/dVc_zF4Z). and in the corresponding Meta announcement (https://lnkd.in/dGR9j_2r).

Dive into the future of AI chatbots with NVIDIA's latest innovation! Their open-source chatbot, tailored for local PC usage, offers a customized GPT large language model (LLM) chatbot directly connected to your data. Using this application, you can generate insightful summaries and relevant results tailored to your unique data by feeding YouTube videos and documents. This cutting-edge technology requires a PC with an RTX 30- or 40-series GPU boasting at least 8GB of VRAM, ensuring seamless integration and optimal performance. #AI #ArtificialIntelligence #Chatbot #NVIDIA #Technology #FutureTech #Innovation

Cerebras Systems: The next OpenAI or the next NVIDIA ? Barron’s: “This New AI Chip Makes Nvidia’s H100 Look Puny in Comparison. The WSE-3 has 4 trillion transistors, which gives it 50 times the computing power of the Nvidia H100 graphics processor, at 80 billion transistors. The chip is 46,255 square millimeters, or about 72 square inches. The H100, by contrast, is a little over 1 square inch. The WSE-3 has 125 petaflops of AI computing power, vs. 4 petaflops for the Nvidia H100. The WSE-3 is 57 times larger, and it has 52 times as many cores as the H100.” Full Dislcosure: I’m an investor in Cerebras Systems https://lnkd.in/gu3gP6NV #ai #openai #llms #cuda #barrons

🚨 NVIDIA has just released a powerful AI model with 70 billion parameters that outperforms both GPT-4 and Claude 3.5 Sonnet. The best part? It's open-source, meaning anyone can use it. This model was trained using a method called reinforcement learning, which helps it get better at tasks over time, and it's ready for businesses to start using right away. You can try it for free using the following link: https://lnkd.in/e9JJNTEv your thoughts on this?

NVIDIA Introduces Nemotron 70B: A New Player in Open-Source AI In a significant development in the AI sector, NVIDIA has allegedly launched the Nemotron 70B, a large language model that could rival the current leaders in the field, including OpenAI’s GPT-4 and Anthropic’s Claude 3.5 Sonnet. With 70 billion parameters, this open-source model is designed to excel in natural language processing tasks and has already shown strong performance across several benchmarks. Key Features: • 70 Billion Parameters: The Nemotron 70B is built with a massive 70 billion parameter architecture, positioning it among the most powerful open-source AI models currently available. • Reinforcement Learning: The model leverages reinforcement learning, enabling it to improve over time with continuous feedback from tasks. • Energy Efficiency: NVIDIA has emphasized that the Nemotron 70B is more energy-efficient compared to many existing models, aligning with corporate sustainability goals. • Performance: It is reported to outperform GPT-4 and Claude 3.5 Sonnet in specific natural language understanding benchmarks, making it a competitive force in industries like finance, healthcare, and customer service. This model’s open-source availability allows businesses and researchers alike to customize it for specific needs, offering a high level of adaptability across sectors. Its combination of power and efficiency makes it a compelling option for enterprises seeking robust AI solutions. Implications for AI Development NVIDIA’s move into large language models with the Nemotron 70B marks a shift in the competitive landscape of AI development. Traditionally known for its dominance in GPU technology, NVIDIA now stands as a strong player in the AI software domain, offering an open-source model that balances performance with practical, energy-efficient applications. As AI continues to evolve, models like the Nemotron 70B could accelerate advancements in AI-driven industries and research. Try for free here https://lnkd.in/eX4Pa6KA More Info https://lnkd.in/e9JqgX32

🚀 Exciting News Alert! 🚀 Unlocking the power of LLM models just got easier with NVIDIA's groundbreaking program! 🌟 🔍 Dive into my latest YouTube video where I reveal how you can access ALL LLM models for FREE using the NVIDIA Build Program. Plus, get a whopping 1000 API credits on the house! If you're serious about leveraging cutting-edge technology to revolutionize your projects, this is a game-changer you can't afford to miss. Click the link below to watch now and unleash the full potential of AI: https://lnkd.in/gN-HvYJJ #NVIDIA #LLMModels #AI #Innovation #TechTrends #YouTubeTutorial #FreeAPICredits