Qwen-72B-Chat
Maintainer: Qwen
138
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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
The Qwen-72B-Chat model is a 72B-parameter version of the large language model series called Qwen, proposed by Alibaba Cloud. It is a Transformer-based large language model that has been pre-trained on a vast amount of data, including web texts, books, code, and more. Based on the pre-trained Qwen-72B model, the Qwen-72B-Chat model has been further trained using alignment techniques to create a large-model-based AI assistant.
The Qwen-72B model features a large-scale high-quality training corpus of over 3 trillion tokens, covering Chinese, English, multilingual texts, code, and mathematics. It demonstrates competitive performance, surpassing existing open-source models on a variety of Chinese and English downstream evaluation tasks. The model also has more comprehensive vocabulary coverage, using over 150K tokens, which makes it more friendly to multiple languages. Additionally, it supports a longer context length of up to 32k tokens.
Model inputs and outputs
Inputs
- Text: The
Qwen-72B-Chatmodel can take in text input, such as prompts or conversations, to generate relevant responses.
Outputs
- Text: The model will generate text output in response to the input, which can be used for a variety of language-related tasks, such as chatbots, content generation, and question answering.
Capabilities
The Qwen-72B-Chat model demonstrates strong performance on a wide range of tasks, including commonsense reasoning, mathematical problem-solving, and code generation. It also exhibits the ability to handle long-context understanding and tool usage, such as calling plugins and APIs through ReAct Prompting.
What can I use it for?
The Qwen-72B-Chat model can be used for a variety of natural language processing tasks, such as building chatbots, generating content, and assisting with research and analysis. Its large-scale training and strong capabilities make it a powerful tool for developers and researchers working on language-related projects.
Things to try
One interesting aspect of the Qwen-72B-Chat model is its ability to handle long-context understanding. By incorporating techniques like NTK-aware interpolation and LogN attention scaling, the model can extend its context length to over 8,000 tokens, making it suitable for tasks that require processing and generating long-form text. Developers can explore how to leverage this capability to build more sophisticated language applications.
Another area to experiment with is the model's tool usage capabilities, which allow it to call external plugins and APIs through ReAct Prompting. Developers can create custom plugins and integrate them with the Qwen-72B-Chat model to expand its functionality and enable it to perform a wider range of tasks.
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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