Huawei Pangu large model
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Huawei Pangu large model application portal. Recently, the editor found that many users are looking for the application address for Huawei Pangu large model. For this purpose, we have compiled a collection of Huawei Pangu large model addresses. You are welcome to click to enter and register online.
For the positioning of the Pangu large model, Huawei’s internal team established the three most critical core design principles: first, the model must be large and can absorb massive amounts of data; second, the network structure must be strong to truly bring out the performance of the model; third, it must have excellent Generalization ability can truly be applied to work scenarios in all walks of life.
Huawei Cloud's official website shows that the Pangu model is composed of multiple large models such as NLP large model, CV large model, multi-modal large model, and scientific computing large model. Through model generalization, it can solve problems that cannot be solved in the traditional AI workshop-style development model. AI scales and industrializes problems and can support a variety of natural language processing tasks, including text generation, text classification, question and answer systems, etc.
Huawei stated that the Pangu NLP large model was jointly developed by Huawei Cloud, Circular Intelligence, and Pengcheng Laboratory, and has leading language understanding and model generation capabilities: in the CLUE list of the authoritative Chinese language understanding evaluation benchmark, the Pangu NLP large model ranked first among all Ranking first in rankings, classifications, and reading comprehension, breaking the world historical record for the three rankings; the total ranking score is 83.046, and multiple sub-task scores lead the industry, making a big step towards human level (85.61).
Specifically, the Pangu NLP large model uses the Encoder-Decoder architecture for the first time, taking into account the understanding and generation capabilities of the NLP large model, ensuring the flexibility of embedding the model in different systems. In downstream applications, only a small number of samples and learnable parameters are needed to complete rapid fine-tuning and downstream adaptation of a large model with a scale of hundreds of billions. This model has good performance in intelligent public opinion and intelligent marketing.
