32K
0.00126
0.00126
72B
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Qwen 2.5 72B Instruct Turbo
Discover Qwen 2.5 Instruct Turbo API's advanced features for developers, including coding support and extensive context handling capabilities.
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Qwen 2.5 Instruct Turbo excels in coding tasks with expansive context capabilities.
Qwen 2.5 72B Instruct Turbo is a state-of-the-art large language model designed for a variety of natural language processing tasks, including instruction following, coding assistance, and mathematical problem-solving.
The model is designed for software developers needing advanced coding support, natural language understanding, and the ability to generate structured outputs like JSON. It excels in scenarios requiring long-form content generation and complex problem-solving.
Primarily supports English, but also capable of understanding and generating text in multiple languages including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
Qwen 2.5 utilizes a transformer architecture, which is well-suited for handling sequential data and enabling effective context management over long inputs.
The model was trained on a diverse dataset comprising various domains, including programming languages, mathematics, and general knowledge. This dataset is designed to enhance the model's understanding and responsiveness across multiple topics.
The training involved hundreds of gigabytes of text data from open-source repositories, academic papers, and web content, ensuring a broad representation of knowledge.
The model's knowledge is current as of September 2024.
Qwen 2.5 was trained on a diverse dataset aimed at minimizing bias. However, ongoing evaluations are necessary to identify any remaining biases in its outputs.
Qwen 2.5 72B Instruct performs particularly well in logical reasoning and math tasks, such as GSM8K (95.8) and MATH (83.1). It also excels in human evaluation and programming benchmarks, scoring 86.6 on HumanEval and 88.2 on MBPP. However, it has relatively lower performance on certain tests, such as GPQA (49.0) and LiveBench 0831 (52.3).
Note that Qwen 2.5 72B Instruct Turbo is faster that the Qwen 2.5 72B Instruct because it has a reduced maximum token limit, resulting in a smaller context window. While the original model can handle up to 128k tokens, the Turbo version is limited to 32k tokens, which enhances its speed by requiring less computational resources for processing inputs. This trade-off makes the Turbo variant more efficient, especially for tasks that don’t need the full 128k token context, while still maintaining strong performance in most use cases.
These two graphs below compare the Quality and Speed performance of Qwen 2.5 72B Instruct and leading AI models.
In the Quality chart, Qwen 2.5 72B Instruct ranks competitively with a score of 75, placing it among the top models like Gemini 1.5 Pro and Claude 3.5 Sonnet, outperforming Llama 3.1 (405B) and GPT-4o models.
The Speed chart, which measures output tokens per second, shows Qwen 2.5 72B Instruct performing at 35 tokens per second, slightly behind Gemini 1.5 Flash and GPT-4o mini, but ahead of other well-known models like o1-preview and Llama 3.1.
This positions Qwen 2.5 72B Instruct as a balanced model, offering a solid blend of both quality and speed for robust AI tasks.
The model is available on the AI/ML API platform as "Qwen/Qwen2.5-72B-Instruct-Turbo".
Detailed API Documentation is available here.
The development of Qwen models adheres to ethical standards aimed at minimizing harm and promoting fairness in AI applications. Continuous monitoring for biases and inappropriate content generation is part of the operational protocol.
Open-source under the Apache License 2.0, allowing both commercial and non-commercial usage rights.
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