Audience
Developers that want a powerful LLM
About Qwen
Qwen LLM refers to a family of large language models (LLMs) developed by Alibaba Cloud's Damo Academy. These models are trained on a massive dataset of text and code, allowing them to understand and generate human-like text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
Here are some key features of Qwen LLMs:
Variety of sizes: The Qwen series ranges from 1.8 billion to 72 billion parameters, offering options for different needs and performance levels.
Open source: Some versions of Qwen are open-source, which means their code is publicly available for anyone to use and modify.
Multilingual support: Qwen can understand and translate multiple languages, including English, Chinese, and French.
Diverse capabilities: Besides generation and translation, Qwen models can be used for tasks like question answering, text summarization, and code generation.
Pricing
Starting Price:
Free
Free Version:
Free Version available.
Integrations
Company Information
Alibaba
Founded: 1999
China
github.com/QwenLM/Qwen
Videos and Screen Captures
Smaug-72B
Smaug-72B is a powerful open-source large language model (LLM) known for several key features:
High Performance: It currently holds the top spot on the Hugging Face Open LLM leaderboard, surpassing models like GPT-3.5 in various benchmarks. This means it excels at tasks like understanding, responding to, and generating human-like text.
Open Source: Unlike many other advanced LLMs, Smaug-72B is freely available for anyone to use and modify, fostering collaboration and innovation in the AI community.
Focus on Reasoning and Math: It specifically shines in handling reasoning and mathematical tasks, attributing this strength to unique fine-tuning techniques developed by Abacus AI, the creators of Smaug-72B.
Based on Qwen-72B: It's technically a fine-tuned version of another powerful LLM called Qwen-72B, released by Alibaba, further improving upon its capabilities.
Overall, Smaug-72B represents a significant step forward in open-source AI.
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Phi-3
A family of powerful, small language models (SLMs) with groundbreaking performance at low cost and low latency. Maximize AI capabilities, lower resource use, and ensure cost-effective generative AI deployments across your applications. Accelerate response times in real-time interactions, autonomous systems, apps requiring low latency, and other critical scenarios. Run Phi-3 in the cloud, at the edge, or on device, resulting in greater deployment and operation flexibility. Phi-3 models were developed in accordance with Microsoft AI principles: accountability, transparency, fairness, reliability and safety, privacy and security, and inclusiveness. Operate effectively in offline environments where data privacy is paramount or connectivity is limited. Generate more coherent, accurate, and contextually relevant outputs with an expanded context window. Deploy at the edge to deliver faster responses.
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Qwen2
Qwen2 is the large language model series developed by Qwen team, Alibaba Cloud.
Qwen2 is a series of large language models developed by the Qwen team at Alibaba Cloud. It includes both base language models and instruction-tuned models, ranging from 0.5 billion to 72 billion parameters, and features both dense models and a Mixture-of-Experts model. The Qwen2 series is designed to surpass most previous open-weight models, including its predecessor Qwen1.5, and to compete with proprietary models across a broad spectrum of benchmarks in language understanding, generation, multilingual capabilities, coding, mathematics, and reasoning.
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Qwen-7B
Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include:
Trained with high-quality pretraining data. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data.
Strong performance. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc.
And more.
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