Fluxgym is a new training tool aimed at AI enthusiasts, designed to lower the barriers to model training. It has extremely low hardware requirements, needing only 12GB of VRAM to conduct AI model training, significantly expanding the participant base. By combining a simple front-end with a powerful back-end, Fluxgym provides an easy-to-use interface, allowing users to initiate the training process in just three simple steps. Its core advantages lie in support for low-spec devices, a simplified user interface, and a smooth training process, enabling AI newcomers to get started quickly.

The release of PaddlePaddle Framework 3.0 features a core upgrade to unified automatic parallelism technology, aimed at simplifying the distributed training process for large models and improving development efficiency. The new version supports mixed parallelism techniques from four-dimensional to five-dimensional, employing various parallel methods such as data parallelism, tensor model parallelism, pipeline parallelism, and grouped parameter slicing parallelism, effectively enhancing the training efficiency of large models. The automatic parallelism technology uses tensor slicing syntax to automatically infer distributed slicing states and add communication operators, reducing the difficulty of development. The principles of automatic parallelism include distributed tensor representation and slicing inference.

NVIDIA has open-sourced two new large models, Nemotron-4-Minitron-4B and Nemotron-4-Minitron-8B, which utilize structured pruning and knowledge distillation for efficient training. This significantly reduces training requirements, minimizing data and computing power consumption. Compared to traditional methods, the new models reduce training token data by 40 times and save 1.8 times in computing costs. By optimizing Llama-3.18B, structured pruning simplifies the model structure, while knowledge distillation enhances performance.

According to the CEO of Anthropic, the current cost of training AI models is as high as $10 billion, and it could rise to $100 billion or even $1 trillion in the next three years. This forecast has sparked concerns about whether the AI bubble is about to burst.Image Source Note: The image was generated by AI, provided by the image authorization service provider Midjourney Dario Amodei points out that as AI models continue to evolve, hardware demand will also grow exponentially, becoming a major

The Brazilian National Data Protection Authority (ANPD) issued a "precautionary measure" prohibiting Meta, Inc. from using its citizens' data to train AI algorithms. This decision immediately suspended the changes made to Meta's privacy policy last month, which added the company's AI training to its data handling disclosures.The ANPD stated: "Following an initial analysis, considering the serious and irreparable risks to users, the agency has temporarily ordered the suspension of the privacy pol

Tencent recently released the 2.0 version of its independently developed Xingmai Network, a significant upgrade that brings a substantial performance boost to large-scale artificial intelligence model training. The new version has achieved breakthroughs in multiple aspects, including network scale, hardware performance, communication protocols, and fault diagnosis. It is reported that in terms of network scale, Xingmai Network 2.0 supports networking of 100,000 card groups in a single cluster, p

Adobe, a name well-esteemed in the creative industry, has earned the reputation of being a "Copyright Guardian" for its stance on copyright protection. However, the company has recently been caught in a storm of public opinion due to a quietly updated service term. This February, Adobe silently updated its product service terms, adding a striking new clause: users must agree that Adobe can access their works automatically and manually, including those protected by confidentiality agreements. Ado

Tencent Cloud recently launched an upgraded version of the Xing Mai Network 2.0, aiming to enhance the efficiency of large-scale model training. In the previous version, the synchronization communication time for the computation results of large models accounted for over 50% of the total, resulting in low efficiency. The new version of Xing Mai Network 2.0 has been upgraded in several aspects:1. Supports networking of up to 100,000 cards in a single cluster, doubling the scale, with a 60% improv

Recently, the Beijing Internet Court held online hearings for four copyright infringement cases filed by illustrators against the developers and operators of AI art software. The plaintiffs in these cases are all renowned illustrators who discovered that their original works, published on a content-sharing platform, were extensively mimicked by the AI art software and used as training material to generate new artworks. These AI-generated artworks not only closely resemble the style of the plaintiffs' works but are also used heavily for commercial purposes, severely harming the interests of the plaintiffs.

The American social media platform Reddit has reached an agreement with Google, valued at approximately $60 million annually. Springer Publishing Group collaborates with OpenAI, becoming the first publishing entity to deeply integrate journalism with artificial intelligence technology. OpenAI has signed a cooperation agreement with Axel Springer, indicating that future large model training may involve payments to data providers. Publishing industry companies possess a wealth of electronic graphic resources and are expected to become important datasets for large model training.

Midjourney announced last night that it plans to start training its video model in January. The V6 version is set to come with significant updates, including notable improvements in text processing, enhanced content coherence, and better accuracy. The focus of the V6 improvements will be the Inpainting feature, with a style adjuster potentially offering a completely new experience. The V7 version signifies a major update, indicating that Midjourney will become a strong competitor in the video generation space. The article reports on the latest developments from Midjourney, including the upcoming V6 version.

The EU plans to support local AI startups in model training by providing access to supercomputer processing power. The early involvement of Mistral in the EU's supercomputer access program may indicate alignment of ideas. The European Commission has announced that dedicated supercomputer access will be provided to AI startups to help them develop large models. This initiative is the latest move in the EU's increased investment in artificial intelligence, aiming to help European companies compete with the US in this critical field.

The AI system A-Lab independently created 41 new materials in 17 days, averaging more than 2 new materials each day, surpassing human speed. The GNoME model developed by Google DeepMind contributed nearly 400,000 new compound data points to material projects. A-Lab successfully synthesized 41 out of 58 predicted materials, achieving a success rate of 71%. GNoME improved the accuracy of structural stability predictions, significantly enhancing the efficiency of material discovery. AI facilitates the rapid manufacturing and testing of new materials, focusing on the development of critical materials for future technologies.

ZTE has launched the latest AI server that supports large model training aimed at the training and inference needs of small to medium-sized models. ZTE has introduced the G5 series servers and plans to release the latest AI server supporting large model training this year.

Shanghai AI Lab has released the large model training toolbox XTuner, which supports various hardware adaptations, significantly lowering the training cost barrier. XTuner is compatible with multiple open-source large models and can execute tasks such as incremental pre-training and instruction fine-tuning. XTuner balances usability and configurability with a built-in standardized process for one-click training.

The ZhiYuan Research Institute has released the world's largest Chinese-English semantic vector model training dataset, MTP, with a data scale of 300 million pairs. MTP is the largest open-source dataset of Chinese-English related text pairs, providing an important foundation for training semantic vector models. The dataset includes Chinese-English text pairs from multiple sources, covering various types such as Q&A, comments, and news. The ZhiYuan Research Institute stated that this data plays a crucial role in training large models and will promote collaborative innovation in artificial intelligence. The release of this dataset is expected to address the shortage of training datasets for Chinese models.

The Megatron-LLaMA framework, jointly developed by Taotian Group and Aicheng Technology, aims to enhance the training performance of large language models while reducing training costs and maintaining compatibility with the LLaMA community, achieving a 176% speedup in training on 32 GPUs.

Minglue Technology has open-sourced the C interface for TensorBoard to monitor the pre-training process of large models and improve training efficiency. By visualizing model parameters and results, the training state can be analyzed, enhancing both the training progress and outcomes of large models. This tool presents training metrics through a multi-dimensional data model, including scalars, histograms, images, and audio.

Data, as a new factor of production, is forming a data industry chain. Large model training requires massive amounts of high-quality data, which is seen as one of the core competitive advantages for future large model companies. The accounting and trading of data face many challenges, such as rights confirmation and security management.