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klotz: multimodal* + llm*

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1. Llama 3.2 Guide: How It Works, Use Cases & More

   Meta releases Llama 3.2, which features small and medium-sized vision LLMs (11B and 90B) alongside lightweight text-only models (1B and 3B). It also introduces the Llama Stack Distribution.

   2024-09-29 Tags: llama 3.2, multimodal, vision, llm by klotz
2. The Next Big Trends in Large Language Model (LLM) Research

   Explores recent trends in LLM research, including multi-modal LLMs, open-source LLMs, domain-specific LLMs, LLM agents, smaller LLMs, and Non-Transformer LLMs. Mentions examples such as OpenAI's Sora, LLM360, BioGPT, StarCoder, and Mamba.

   2024-07-05 Tags: llm, multimodal, agent, small language models, domain language models by klotz
3. How to Fine-tune Florence-2 for Object Detection Tasks

   This article provides a step-by-step guide on fine-tuning the Florence-2 model for object detection tasks, including loading the pre-trained model, fine-tuning with a custom dataset, and evaluating the model's performance.

   2024-06-26 Tags: florence-2, object detection, multimodal, llm, vision, microsoft, fine tuning by klotz
4. emcf/thepipe: Feed PDFs, URLs, Slides, YouTube, GitHub, and more into Vision-Language models with one line of code ⚡

   The Pipe is a multimodal-first tool for feeding files and web pages into vision-language models such as GPT-4V. It is best for LLM and RAG applications that want to support comprehensive textual and visual understanding across a wide range of data sources. The Pipe is available as a 24/7 hosted API at thepi.pe, or it can be set up locally to let you run the compute.

   2024-05-04 Tags: github, thepipe, vision-language models, multimodal, llm by klotz

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