Update README.md

README.md

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 **QwQ-32B-Preview** is an experimental research model developed by the Qwen Team, focused on advancing AI reasoning capabilities. As a preview release, it demonstrates promising analytical abilities while having several important limitations:
 
-1. **Language Mixing and Code-Switching**: The model may occasionally mix different languages or switch between languages unexpectedly in its responses. This includes both natural language code-switching (e.g., mixing English with other languages) and technical language variations (e.g., inconsistent use of technical terminology). While this reflects the model's broad knowledge base, it can sometimes affect response coherence and clarity.
-2. **Recursive Reasoning Loops**: When tackling complex logical problems, the model may occasionally enter recursive reasoning patterns where it circles through similar lines of thought without converging on a definitive answer. This can manifest as repetitive explanations or circular logic chains that, while internally consistent, fail to reach a conclusive resolution. While this behavior reflects the model's attempt at thorough analysis, it can sometimes lead to unnecessarily lengthy responses without adding substantive value.
-3. **Safety and Ethical Considerations**: While the model incorporates basic safety measures, these require significant enhancement to ensure consistently reliable and secure performance across diverse use cases. The model may occasionally generate responses that could be considered inappropriate, biased, or potentially harmful. Additionally, like other large language models, it may be susceptible to adversarial prompting or misuse. Users should exercise caution and implement appropriate safeguards when deploying the model in production environments. We are actively working on improving these safety mechanisms in future releases.
-4. **Performance and Benchmark Limitations**: While QwQ-32B-Preview demonstrates strong capabilities in mathematics and coding tasks, there is substantial room for improvement across other benchmark evaluations. The model's performance can be inconsistent in areas such as common sense reasoning, multi-step logical deduction, and nuanced language understanding tasks. Additionally, its performance may vary significantly depending on the complexity and domain specificity of the task at hand. We acknowledge these limitations and are actively working to enhance the model's capabilities across a broader spectrum of benchmarks through continued training and architectural improvements.
+1. **Language Mixing and Code-Switching**: The model may occasionally mix languages or switch between them unexpectedly, affecting response coherence and clarity.
+2. **Recursive Reasoning Loops**: When handling complex logical problems, the model may fall into repetitive reasoning patterns, leading to circular logic without reaching a conclusive answer.
+3. **Safety and Ethical Considerations**: The model may occasionally generate inappropriate, biased, or harmful content and is susceptible to adversarial prompting. Users should implement safeguards when deploying the model. We are actively improving these safety mechanisms.
+4. **Performance and Benchmark Limitations**: While QwQ-32B-Preview excels in mathematics and coding, it has inconsistent performance in common sense reasoning, multi-step deduction, and nuanced language tasks. Performance varies based on task complexity and domain specificity. We are working to improve its capabilities across a broader range of benchmarks.
 
 **Specification**:
 - Type: Causal Language Models