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…
