10/02/2025
🔍 Are you concerned about the privacy of your data in language models? 🤔💭
🔒 I recently came across an interesting discussion about building a new LLM model with enhanced privacy. As we know, most LLM models are open sources, meaning the data we share while discussing various topics belongs to the likes of OpenAI, Deep SEQ, Gemini, etc. 😮
🌐 However, some entrepreneurs are exploring the idea of local servers, where you can have your own language model and keep the data shared on that LLM on your own server. This way, other companies won't have access to your data. 🙌
💡 It sounds like a great idea, right? But here's the catch - the person proposing this project was looking to raise a whopping 5 billion dollars to make it happen! 💸
🚀 Meanwhile, Deep SEQ claims to have finished their v3 model, boasting about its efficiency. They claim it took them only 3 million chip hours, which is just 1/10th of what Meta's Llama 3.1 required (30 million chip hours). 😱
📊 Keep in mind that Meta already had access to vast amounts of data from applications like Facebook, Instagram, and WhatsApp, making their training process easier. Yet, Deep SEQ's model supposedly matches up to Meta's performance. 🤔
🌐 However, we should also consider the lack of transparency surrounding Chinese companies, especially when it comes to economic competition and craftsmanship. While I can't investigate the truth behind these claims, I do have my doubts. 🤷♀️
🔍 Let's wait for more information before drawing conclusions. It's essential to understand the full story and implications of these advancements in language models. 📚