Qwen-72B
Maintainer: Qwen
324
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
Qwen-72B is the 72B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-72B 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-72B, Qwen releases Qwen-72B-Chat, a large-model-based AI assistant, which is trained with alignment techniques.
Key features of Qwen-72B include:
- Large-scale high-quality training corpora: It is pretrained on over 3 trillion tokens, including Chinese, English, multilingual texts, code, and mathematics, covering general and professional fields.
- Competitive performance: It significantly surpasses existing open-source models on multiple Chinese and English downstream evaluation tasks.
- More comprehensive vocabulary coverage: Compared to other models, Qwen-72B uses a vocabulary of over 150K tokens, allowing for more efficient encoding of multiple languages.
- Longer context support: Qwen-72B supports a context length of up to 32k tokens.
Model inputs and outputs
Inputs
- Text: Qwen-72B accepts text inputs in a variety of languages, including Chinese, English, and others.
Outputs
- Text: Qwen-72B generates fluent and coherent text outputs in response to the input, drawing upon its broad knowledge base.
- Code: In addition to natural language, Qwen-72B can also generate code in various programming languages.
Capabilities
Qwen-72B demonstrates impressive performance on a wide range of tasks, including commonsense reasoning, language understanding, mathematical problem-solving, and code generation. For example, it achieves state-of-the-art results on benchmarks like MMLU, C-Eval, and HumanEval, outperforming many other large language models of similar or even larger scale.
What can I use it for?
With its broad capabilities, Qwen-72B can be leveraged for a variety of applications, such as:
- Content creation: Generating high-quality text, articles, stories, and dialogues in multiple languages.
- Conversational AI: Powering intelligent chatbots and virtual assistants with advanced language understanding and generation abilities.
- Code generation and programming: Assisting developers with tasks like code completion, refactoring, and even full-fledged program generation.
- Multilingual applications: Developing multilingual applications that can seamlessly handle and translate between various languages.
Things to try
One interesting aspect of Qwen-72B is its ability to handle long-form text and extended context. You could try generating coherent and relevant output based on lengthy prompts or multi-turn dialogues, exploring how the model maintains context and produces consistent responses over time.
Another area to experiment with is the model's code generation capabilities. You could provide Qwen-72B with programming prompts or partially completed code snippets and observe how it can extend and refine the code to solve specific tasks or implement desired functionalities.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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