Where are small Models like Qwen3 0.6B and Qwen3.5 0.8B used ? Huggingface shows 2.88 million downloads this month.[D]

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I noticed a post about the Qwen3.5 and Qwen3 models with 2.88 million downloads per month, indicating their popularity in research workflows. However, it seems these smaller models struggle with fundamental issues such as surface-level understanding of concepts, unreliable JSON outputs, and slow response times.

• They lack a deep semantic understanding, which can lead to confusion or incorrect interpretations of tasks.
• Their JSON outputs are often flawed, requiring additional validation layers that consume significant development time.
• The models themselves are quite slow, despite efforts to improve performance.

These observations highlight the challenges faced by researchers and developers when working with smaller model sizes like Qwen3.5 and Qwen3. The community’s experience underscores the need for more robust and efficient solutions in this space.

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### Takeaways:
– **User Experience**: Smaller models like Qwen3.5 face significant usability issues, including shallow understanding of tasks and unreliable JSON outputs.
– **Performance Bottlenecks**: Despite recent improvements, these models remain slow, which can hinder productivity in research workflows.
– **Community Curiosity**: There is a notable curiosity about how the community is actually using these smaller models, suggesting potential gaps or unmet needs.

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