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Qwen2.5 Turbo
Overview :
Qwen2.5-Turbo is an innovative language model developed by Alibaba's team, optimized for processing extremely long texts. It supports a context of up to 1 million tokens, which is equivalent to approximately 1 million English words or 1.5 million Chinese characters. The model achieved a 100% accuracy rate in the 1M-token Passkey Retrieval task and scored 93.1 in the RULER long text evaluation benchmarks, surpassing both GPT-4 and GLM4-9B-1M. Qwen2.5-Turbo not only excels in long text handling but also maintains high performance in short text processing, offering exceptional cost-effectiveness at only 0.3 yuan per million tokens processed.
Target Users :
The target audience for Qwen2.5-Turbo includes developers, data scientists, and enterprises handling large volumes of text data. It is well-suited for users needing to extract information, generate summaries, or conduct complex text analyses within long texts. Its efficient long text processing capability and cost-effectiveness also make it an ideal choice for small to medium-sized enterprises with limited budgets seeking high-performance text processing solutions.
Use Cases
Using Qwen2.5-Turbo to process 10 full-length novels and generate detailed summaries for each.
Converting 150 hours of speech transcripts into concise meeting minutes.
Providing contextual understanding for 30,000 lines of code to assist in code generation and error detection.
Features
Supports a context length of up to 1 million tokens, capable of handling extremely long texts.
Achieved 100% accuracy in the 1M-token Passkey Retrieval task.
Scored 93.1 in the RULER long text evaluation benchmarks, outperforming GPT-4.
Integrates sparse attention mechanisms, reducing the time to generate the first token from 4.9 minutes to 68 seconds.
Processing cost is highly competitive at 0.3 yuan per million tokens.
Maintains high performance in short text handling, comparable to GPT-4o-mini.
Accessible via API services, compatible with standard Qwen API and OpenAI API.
How to Use
1. Prepare a long text file, for example, 'example.txt'.
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2. Write a Python script to import the necessary libraries.
3. Read the contents of the long text file and add user input.
4. Set the environment variable 'YOUR_API_KEY' to obtain your API key.
5. Create an OpenAI client instance by specifying your API key and base URL.
6. Call the client's chat.completions.create method with the model name and message.
7. Print the returned completion results.
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