Qwen2-72B-Instruct
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This model is stronger than the last generation of Qwen 1.5. The model's linguistic proficiency has been roadened to 27 additional languages, demonstrated state-of-the-art results across a multitude of evaluations, and the context length support was increased up to an impressive 128K tokens.
This enhancement allows for more comprehensive and contextually rich interactions, making Qwen2 an even more powerful tool for a variety of applications. Qwen2 builds on the Transformer architecture, adding advanced features like SwiGLU activation, attention QKV bias, group query attention, a mixture of sliding window attention, and more for improved efficiency and focus when processing information.
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Only model
and messages
are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding , which lists all available parameters along with notes on how to use them.
If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our .
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
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