Image created with Flux Pro v1.1 Ultra. Image prompt: photorealistic still image of a middle-aged man standing behind a woman, woman covering part of her face with her hand, man looking over her shoulder, both illuminated with warm stadium jumbotron lighting, natural skin tones, subtle lens flare, shallow depth of field, exact color temperature of a live event projection, woman holding a file folder labeled “RAG,” man holding a document with highlighted text, cinematic realism –no text, captions, watermarks
Pierre and team really cooked with this vision language model (VLM)! Excited for you to try it out! 111B open parameters”” / X https://x.com/JayAlammar/status/1950931480349143259
RT @1vnzh: Command A Vision – SOTA enterprisemaxx multimodal model – Outperforms GPT 4.1, Llama 4 Maverick, and Mistral Medium 3 in enterpr…”” / X https://x.com/aidangomez/status/1950927454383616343
RT @nickfrosst: cohere vision model 🙂 weights on huggingface https://x.com/andrew_n_carr/status/1951068402090647608
RT @_avichawla: You can now deploy any ML model, RAG, or Agent as an MCP server. And it takes just 10 lines of code. Here’s a step-by-ste…”” / X https://x.com/_avichawla/status/1950282234893656101
LLMs can make sense of retrieved context because of how transformers work. In one of the lessons from the Retrieval Augmented Generation (RAG) course, we unpack how LLMs process augmented prompts using token embeddings, positional vectors, and multi-head attention. Understanding https://x.com/DeepLearningAI/status/1950979807623139539
