Image created with gemini-2.5-flash-image with claude-sonnet-4-5. Image prompt: Create a 16:9 cinematic split-screen poster. LEFT SIDE (40% width): – A friendly, plush toy llama standing on a desk beside a notebook open to simple diagrams of language model layers and tokens, with a couple of colored pens nearby. – The background is a turquoise / teal abstract field made of stylized blue rods or data fibers, lightly echoing the model’s architecture. – Use warm, natural lighting. No glowing mascots, no neon. RIGHT SIDE (60% width): – A green-toned abstract aerial forest canopy texture, evoking a living ecosystem of open models. – Two clean rounded rectangles stacked vertically near the center-right. – The TOP rectangle contains the text: “Llama”. – The BOTTOM rectangle contains the text: “2025/10/10”. – Clean sans-serif font, dark green or charcoal. OVERALL STYLE: – Playful but calm, grounded in research. – No official branding. – Preserve the turquoise/forest split-screen.
⚡ Building Agentic Workflows for Technical Document Analysis Check out our new end-to-end example by @jerryjliu0: an agentic workflow that extracts structured data from solar panel datasheets and automatically generates compliance reports against design requirements. 🔄 https://x.com/llama_index/status/1975587234247286921
1/5 Releasing Jamba Reasoning 3B under Apache 2.0: Hybrid SSM-Transformer architecture that tops accuracy & speed across record context lengths. e.g. 3-5X faster than Llama 3.2 3B and Qwen3 4B at 32K tokens. https://x.com/AI21Labs/status/1975917052906078528
Researchers introduced GAIN-RL, a method that fine-tunes language models by training on the most useful examples first. It ranks data using a simple internal signal from the model. On Qwen 2.5 and Llama 3.2, this method matched baseline accuracy in 70 to 80 epochs instead of https://x.com/DeepLearningAI/status/1974640684528243151
