🦙 Meta AI Releases Llama 3: 8B & 70B Models! 🚀 Meta has released two new Llama 3 models : 8B parameter model outperforms 70B Llama 2 🤯 70B parameter model is competitive with Gemini Pro 1.5 and Claude 3 Sonnet 💪 (more models are coming soon!) 405B model in the works, nearing GPT-4 level 👀 🔑 Key Takeaways: Trained on 15T tokens, 7x more than Llama 2 8K context window, shorter than some recent models Performs well on reasoning benchmarks like GSM8K Supports commercial use with some license restrictions 🧪 Trying it Out: Available via Ollama, HuggingFace, API services Can be run locally with quantization Responds well to chain-of-thought & ReACT prompts Some multilingual support but dedicated model likely coming 📜 License Notes: - Can't use Llama 3 to train other large models - Must include "Llama 3" in name of fine-tuned versions - Review prohibited use cases before commercial use The Llama 3 models look promising and signal continued rapid progress in open LLMs. Expect to see powerful fine-tuned versions and new capabilities soon! 🦙🔥 #Llama3 #OpenLLMs #LLMProgress
Llama 3 - 8B & 70B Deep Dive
https://www.youtube.com/
Harry Vu
2mo
Love it. You can try it on groq for free now. Hat off to the Meta AI team. Thanks for a thorough deep dive.
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
2moThe release of Llama 3 by Meta AI marks a significant advancement in open LLMs, showcasing improvements in model performance and scalability. You talked about the impressive 8B parameter model outperforming its predecessor and competing with other leading models like Gemini Pro 1.5 and Claude 3 Sonnet. Considering the licensing restrictions and commercial applications, how would you strategically navigate the limitations while leveraging Llama 3's capabilities for developing highly specialized AI solutions? For instance, if imagine implementing an AI system for anomaly detection in satellite imagery, how would you technically utilize Llama 3's features to enhance the model's accuracy and efficiency in identifying subtle anomalies within vast datasets?