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Recently, AIbase obtained the latest information from social media platforms and learned about Shisa.AI, a HuggingFace model provider specializing in Japanese fine-tuning. Its newly released bilingual Japanese-English model has attracted significant attention in the industry. This article will provide you with an in-depth analysis of Shisa.AI's latest achievements and its breakthrough advancements in the field of Japanese AI. Shisa V2405B: The birth of Japan's strongest open source model According to AIbase's understanding, Shisa.AI has recently released a Llama3.1-based S

Recently, Tencent Cloud Development (CloudBase) has made significant progress in the field of intelligent coding and launched "CloudBase AI ToolKit", which introduces the first backend service for intelligent programming in China. It becomes the first serverless development platform in China that integrates with mainstream AI coding tools, allowing AI coding to evolve from simple static web page generation (from 0 to 1) to full-cycle application development, deployment, and operation with commercial value (from 1 to N). During the development phase, even with the help of AI tools, developers used to...

On May 8th, OpenAI o4-mini launched with Reinforcement Fine-Tuning. The combination of these two technologies completely transforms the cost structure and technical threshold for AI specialization, enabling enterprises to rapidly turn general-purpose AI into expert systems in specific fields using only a small amount of training data. The leap from general intelligence to expert-level AI is highlighted by the Reinforcement Fine-Tuning technology at the core of this release. Unlike traditional supervised fine-tuning, RFT is based on reinforcement learning algorithms, driven by reward-based training.

This project details the creation of Pusa, a high-quality open-source video model built with a budget under $100. It leverages Mochi fine-tuning for efficient and cost-effective training, demonstrating the feasibility of achieving impressive results with minimal resources.

DeepSeek AI, a prominent Chinese artificial intelligence research lab, following its powerful open-source language model DeepSeek-R1, has achieved another significant breakthrough in the field of Large Language Models (LLMs). Recently, DeepSeek AI officially launched an innovative technology called Self-Principled Critique Tuning (SPCT), aimed at building more general-purpose and scalable AI reward models.

Skywork AI's research team recently unveiled SkyReels-A2, a groundbreaking video generation framework pushing the boundaries of controllable video synthesis. This Element-to-Video (E2V) framework generates natural videos from text prompts by composing diverse visual elements (characters, objects, backgrounds), maintaining high fidelity to reference images. SkyReels-A2's core lies in its sophisticated data processing pipeline. The research team designed a comprehensive data construction pipeline.

A groundbreaking achievement in quantum computing has been reached with the successful fine-tuning of a billion-parameter AI model on Origin Quantum's Wukong, marking a significant milestone in the development of quantum-enhanced AI.

Researchers from DeepSeek and Tsinghua University recently published a new paper exploring scaling methods for reward model inference, seemingly advancing DeepSeek R2. Reinforcement learning is widely used in the large-scale post-training phase of large language models, but faces the challenge of obtaining accurate reward signals for these models. The researchers found that using pointwise generative reward modeling (GRM) improves model adaptability and scalability during inference. To this end, they propose Self-Principle Calibration Tuning (SPCT) learning.

Data intelligence company Databricks recently unveiled a novel large language model fine-tuning method – TAO (Test-time Adaptive Optimization). This technology offers new hope for the advancement of open-source models. By leveraging unlabeled data and reinforcement learning, TAO excels in reducing enterprise costs while achieving remarkable results across various benchmark tests. According to tech news outlet NeoWin, TAO...

Large Language Models (LLMs) are constantly evolving in the field of artificial intelligence. Researchers from Carnegie Mellon University (CMU) and HuggingFace recently introduced a new method called Meta Reinforcement Fine-Tuning (MRT). This method aims to optimize the computational efficiency of LLMs during testing, particularly excelling in solving complex reasoning problems. Studies show that existing LLMs struggle with...

A detailed interpretation of the GPT-4.5 system card report released by OpenAI on February 27, 2025. This report comprehensively covers the development, capabilities, safety evaluations, and preparedness framework assessment of the GPT-4.5 model, aiming to showcase its advancements and potential risks, and to explain OpenAI's mitigation strategies.

Apple has quietly released its latest budget iPhone, the iPhone 16e, priced at $599, without a major launch event. Unlike previous high-profile releases, this launch was understated, with CEO Tim Cook announcing the news via Twitter. The iPhone 16e doesn't boast groundbreaking innovations but integrates design elements from previous iPhones to ensure reliability at a lower cost. The new model's design resembles earlier iPhone models.

DeepSeek announced a significant price adjustment for API calls during a promotional period. DeepSeek-V3 will be discounted to 50% of its original price, while DeepSeek-R1 will be reduced to a remarkable 25%. This initiative aims to encourage users to leverage platform resources during off-peak hours, enjoying more cost-effective and seamless service.

A collaborative team from Ant Group, Zhejiang University, University of Liverpool, and East China Normal University presented ScaleOT, a novel cross-domain fine-tuning framework, at AAAI 2025. This framework achieves a 50% improvement in privacy protection and a remarkable 90% reduction in computational cost compared to knowledge distillation, without compromising model performance. ScaleOT offers a highly efficient and lightweight solution for cross-domain fine-tuning of billion-parameter models. The innovative nature of the paper secured it an oral presentation at AAAI.