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The latest advancements in AI technology showcase the capabilities of open-source models like Llama 3 and Phi 3. Llama 3 models, including 8b and 70b versions, are being fine-tuned for improved performance and efficiency. Various tests and comparisons highlight the speed, efficiency, and long-context capabilities of Llama 3 models. Companies like GroqInc and FireworksAI_HQ are leveraging Llama 3 for fast, serverless inference. Tools like QLoRA are enhancing the performance of Llama 3 models, making them faster and more memory-efficient. The AI community is actively exploring and extending the context lengths of Llama 3 models, with notable achievements in increasing context lengths to 160K tokens and beyond.
Want to learn how to build a sophisticated question-answering (Q&A) chatbot using RAG (Retrieval Augmented Generation) with @Ollama, @LangChainAI, @milvusio and Llama 3? You're in luck, this step-by-step tutorial shows you the code each step of the way. https://t.co/FnHTpuxXv3
Big week for open-source AI. In just a few days, we've seen two major models launch. Meta Llama 3 and Microsoft Phi-3, each claiming to outperform the other. So I conducted complex coding tests: š§µ https://t.co/0MU7uCPC1t
Colossal-Inference now supports Llama 3 inference acceleration. They report a ~20% enhancement in training efficiency for Llama 3 8B and 70B and outperforming alternative inference solutions such as vLLM. This is why open-source AI matters. There are all kinds of innovationsā¦ https://t.co/BDlR2iGKo2 https://t.co/2r9WCBKQBP
