a bath towel with a llama on it hangs from a towel rack.
“With Llama 3.2 we released our first-ever lightweight Llama models: 1B & 3B. These models empower developers to build personalized, on-device agentic applications with capabilities like summarization, tool use and RAG where data never leaves the device.
“More data makes RAG applications worse, not better. Relying on vector similarity search doesn’t scale, and most people aren’t talking about this. This is counterintuitive, but experiments don’t lie: As you add more documents to a vector-based RAG system, its retrieval
Advanced RAG course
“RAGLAB, great for standardizing RAG research, modular design and fair comparisons. Conducts fair comparison of 6 RAG algorithms across 10 benchmarks Key points 🛠️: – Modular architecture for each RAG component – Standardizes key experimental variables: • Generator fine-tuning https://twitter.com/rohanpaul_ai/status/1838259514237464893
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