Xiaomi Mimo 2.6 live post-training dashboard (mimo.xiaomi.com)

Article summary

The article appears to discuss Xiaomi's Mimo 2.6 live post-training dashboard, which provides transparency into the model's training process. The dashboard shows the model's performance on various benchmarks during training. The discussion revolves around the implications of this transparency and its potential impact on the AI industry. The model's training data and methodology are also topics of interest.

Main themes

• AI model transparency
• open models
• benchmarking and contamination
• model performance and comparison
• industry trends and strategies
• AI development and methodology

What commenters say

• Some commenters appreciate the transparency provided by Xiaomi's dashboard, seeing it as a positive step in the AI industry.
• Others speculate that this transparency may be a strategic move to head off accusations about distillation or to promote open models.
• There is disagreement about the effectiveness of Mimo models compared to other models, such as Qwen and GLM.
• Some commenters note that the use of benchmarks during training may lead to contamination, while others see it as a necessary step in evaluating model performance.
• The discussion also touches on the potential benefits and drawbacks of open models, including the potential for overfitting and the importance of transparency in AI development.
• Some commenters are impressed by the capabilities of certain models, such as Qwen, and their ability to run locally with minimal resources.
• There is also a comparison of the performance and cost of different models, including Mimo and Qwen.