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…
Large language models (LLMs) have made significant strides in reasoning capabilities, exemplified by breakthrough systems like OpenAI o1 and DeepSeekR1, which utilize test-time compute for search and reinforcement learning to optimize performance. Despite this progress, current methodologies face critical challenges…
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data. AI…
