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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.

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.

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.

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.

During a continuous 12-day live event, OpenAI released the O1 model API to developers on the 9th day and announced significant upgrades to the real-time API, supporting WebRTC technology. Starting from the release date, OpenAI will provide access to the O1 API for developers at usage level 5. Compared to the previous preview version, this updated O1 model API achieves a 60% reduction in thinking costs and adds advanced visual processing capabilities. Meanwhile, GPT-4o has improved in audio processing.

Recently, a research team from Shanghai Jiao Tong University and Harvard University introduced a novel model fine-tuning method — LoRA-Dash. This new approach claims to be more efficient than existing LoRA methods, particularly in the fine-tuning of specific tasks, achieving the same results with an 8 to 16 times reduction in the number of parameters. This is undoubtedly a major breakthrough for fine-tuning tasks that require substantial computational resources. In the context of the rapid development of large-scale language models, the demand for fine-tuning specific tasks is steadily increasing. However, fine-tuning often

Baidu Intelligent Cloud's Qianfan Platform has recently announced its support for customized fine-tuning services for the flagship large model ERNIE 4.0 Turbo. Enterprise clients can apply for this service through the Baidu Intelligent Cloud official website to meet their specific business needs and data characteristics, thereby training a large model that is more suitable for their business and significantly improving the effectiveness of business applications.

OpenAI has announced that third-party developers can fine-tune the newly introduced multimodal large model GPT-4o. This feature allows developers to adjust the model's behavior based on specific needs, such as changing the tone, following instructions, or improving the accuracy of technical tasks. Fine-tuning can achieve significant performance improvements with only a small amount of training data and is easy to operate. OpenAI is providing developers with a free allowance of 1 million tokens daily until September 23, as well as a daily free allowance of 2 million tokens for the GPT-4o mini model.

PanoFree is an innovative multi-view image generation technology that can produce various panoramic images, including long images, 360-degree panoramas, and skyboxes, without the need for fine-tuning. Its core lies in addressing consistency issues and artifacts during the generation process through an iterative deformation and repair process while enhancing cross-view awareness. PanoFree employs techniques such as cross-view guidance, risk area estimation, erasure, and symmetric bidirectional guidance, combined with guided semantic and density control to ensure the preservation of scene structure. Experiments show that PanoFree achieves ...

OpenAI has announced the launch of GPT-4o mini, offering daily free fine-tuning with 2 million training tokens until September 23rd, aimed at advancing AI technology and fostering developer innovation. This model boasts performance on par with GPT-4o but is priced at only 1/20th of the latter, with significant advantages in training costs, context length, and intelligence levels. After the free period, services will continue to be offered at discounted rates. The fine-tuning capabilities of GPT-4o mini will be initially available to enterprise clients and specific developer tiers, with future expansion planned.

Microsoft has announced the launch of serverless fine-tuning capabilities for the Phi-3 small language model, enabling developers to easily adjust model performance on the Azure AI platform without managing underlying infrastructure, and it is free for initial use. The Phi-3 model, with 3 billion parameters, is suitable for enterprise developers, offering efficient performance at a lower cost. Although it has fewer parameters compared to OpenAI's GPT-3.5, it performs similarly across various applications. Serverless fine-tuning simplifies operations, reduces barriers, and supports the fine-tuning of small to medium models.

Recently, leading artificial intelligence company OpenAI announced the launch of fine-tuning capabilities for its new small-scale model, GPT-4o mini, aimed at enhancing the model's performance in specific application scenarios. This move will provide developers and enterprise users with more customized AI solutions. According to official OpenAI news, from today until September 23, users can fine-tune GPT-4o mini for free up to 2 million training tokens per day. Any excess will be charged at $3.0

In the realm of artificial intelligence, Large Language Models (LLMs) are renowned for their exceptional natural language processing capabilities. However, deploying these models in practical applications faces significant challenges due to their high computational costs and memory footprint during the inference phase. To address this issue, researchers have been exploring ways to enhance the efficiency of LLMs. Recently, a method called Q-Sparse has garnered widespread attention.Q-Sparse is a s

In the field of artificial intelligence, large language models (LLMs) such as GPT-3 and Llama-2 have made significant strides, capable of accurately understanding and generating human language. However, the vast number of parameters in these models poses challenges in terms of the significant computational resources required for both training and deployment, particularly in resource-constrained environments.Paper Access: https://arxiv.org/html/2406.10260v1Traditionally, to achieve a balance betw