The race for superior language AI continues, and Chinese tech giant Alibaba has unleashed a new contender – meet Qwen-72B. As Alibaba’s latest foray into large language models, Qwen-72B represents a massive leap forward with its towering 3 trillion parameters trained on a data mountain of 3 trillion tokens.
Now released as an open source project for all, Qwen-72B blows past previous benchmarks for scale and training. This enormous foundation empowers it to reach new heights in language understanding and generation. Key upgrades include doubling its contextual window to process longer texts as well as enhanced prompt programming for easily customizing for different uses.
Early testing shows this bigger, smarter model already surpassing others in areas like conversational ability, fact recall, summarization and even translation. While researchers continue pushing its paces, Qwen-72B stands poised to expand the frontiers for a new generation of language AIs. For any industry where language interfaces play a key role, more powerful systems like this could soon unlock new potential.
The open-sourcing also comes with a smaller yet still impressive 1.8 billion parameter model available, further expanding access and ability to build specialized implementations. As one of the biggest leaps forward in language AI yet seen, Qwen-72B may spark a new wave of innovation to utilize such immense knowledge and learning capacity.
Qwen-72B Details and Capabilities
Qwen-72B has an expanded context window length and enhanced system prompt capability, allowing users to customize their own AI assistant with just a single prompt. Qwen-1.8B is also released, which strikes a balance between maintaining essential functionalities and maximizing efficiency. It is capable of generating 2K-length text content with just 3GB of GPU memory. The post also mentions the scale of the corpus, which reaches over 3T tokens after deduplication and filtration, encompassing web text, encyclopedias, books, code, mathematics, and various domains.
We can take a look here at their benchmark releases. They selected MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, CMMLU, which are currently popular benchmarks, to test the model’s Chinese and English knowledge capabilities, translation, mathematical reasoning, coding and other capabilities. The benchmark indicates that the Qwen model outperform the similarly sized open-source models on all tasks. But again we have to be wary of benchmarks, since we see this time and time again, they only tell us so much.
Qwen-72B is a large language model with 72 billion parameters, based on the Transformer architecture. It is pretrained on a diverse range of data, including web texts, books, and code, and it has been optimized for performance on various downstream tasks, such as commonsense reasoning, code, and mathematics.
Open Sourcing and Accessibility
The model is completely open sourced under Apache license. he model’s code and checkpoints are open to research purposes and commercial use, and the authors have provided evaluation scripts to help users reproduce the model’s performance.
As we reflect on models like Qwen-72B and the rapid progress in language AI, an exciting vision of the future comes into view. With Qwen-72B demonstrating new heights in natural language processing and comprehension, one can’t help but wonder what could be possible by combining these strengths with innovations happening elsewhere.
For example, DeepSeek another open sourced model which was released this week, had profound abilities for programming tasks. One can imagine a future language model that blends Qwen-72B’s language mastery with DeepSeek ‘s coding skills and Constitutional AI’s logical foundations. Such an AI could have the well-rounded intelligence to surpass narrow benchmarks and excel in multifaceted real-world challenges.
The open-source community will play a key role in this future as it enables collaborative innovation between different models and paradigms. With companies like Alibaba open-sourcing their work as well, researchers worldwide can build upon each other’s breakthroughs.
There is still a long path ahead, but the possibilities make the journey exciting. As many push the boundaries – each with their own strengths – we inch closer to broader and more beneficial AI applications. And we look forward to the creative solutions that emerge as these technologies become more accessible.
The future remains promising, and if the recent progress is any indication, this new era of multifaceted and collaborative AI advancement could lead us to profound innovations that make the world a little bit better.