LLMs have shown impressive promise in conducting diagnostic conversations, particularly through text-based interactions. However, their evaluation and application have largely ignored the multimodal nature of real-world clinical settings, especially in remote care delivery, where images, lab reports, and other medical…
In this hands-on tutorial, we’ll learn how to seamlessly connect Claude Desktop to real-time web search and content-extraction capabilities using Tavily AI’s Model Context Protocol (MCP) server and the Smithery client. We’ll begin by reviewing the Tavily homepage and dashboard,…
Meta AI has released Llama Prompt Ops, a Python package designed to streamline the process of adapting prompts for Llama models. This open-source tool is built to help developers and researchers improve prompt effectiveness by transforming inputs that work well…
IBM has introduced a preview of Granite 4.0 Tiny, the smallest member of its upcoming Granite 4.0 family of language models. Released under the Apache 2.0 license, this compact model is designed for long-context tasks and instruction-following scenarios, striking a…
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Frontier AI companies show advancement toward artificial general intelligence (AGI), creating a need for techniques to ensure these powerful systems remain controllable and beneficial. A major approach to this challenge involves methods like Recursive Reward Modeling, Iterated Amplification, and Scalable…
from transformers import Blip2Processor, Blip2ForConditionalGeneration import torch from PIL import Image import requests import matplotlib.pyplot as plt import numpy as np from io import BytesIO # Load BLIP-2 model processor = Blip2Processor.from_pretrained(“Salesforce”) model = Blip2ForConditionalGeneration.from_pretrained(“Salesforce”, torch_dtype=torch.float16) if torch.cuda.is_available(): model =…
Fine-tuning remains a cornerstone technique for adapting general-purpose pre-trained large language models (LLMs) models (also called foundation models) to serve more specialized, high-value downstream tasks, even as zero- and few-shot methods gain traction. Source link
Recent advancements in LLMs such as OpenAI-o1, DeepSeek-R1, and Kimi-1.5 have significantly improved their performance on complex mathematical reasoning tasks. Reinforcement Learning with Verifiable Reward (RLVR) is a key contributor to these improvements, which uses rule-based rewards, typically a binary…
In this tutorial, we’ll build an MCP server that integrates Airbnb and Excel, and connect it with Cursor IDE. Using natural language, you’ll be able to fetch Airbnb listings for a specific date range and location, and automatically store them…
