parameter

DeepSeek launches the biggest LLM in open source history… “Caught up with GPT-4o”

China's DeepSeek has unveiled 'DeepSeek-V3', the biggest open source large language model (LLM) ever. It was emphasized that this model has performance that surpasses existing open source models similar to Meta's 'Rama 3.1 405B'...

The day after tomorrow, the 102B open source model with ‘strongest Korean performance’ revealed… “Outperforms each GPT-4o and Q12”

MOREH (CEO Jo Kang-won), a specialist in artificial intelligence (AI) infrastructure solutions, has opened its self-developed Korean foundation large language model (LLM) 'Llama-3-Motif-102B' to Hugging Face. It was announced on the third that it...

“Trump may implement ‘parameter cap system’ to compete with China in AI”

There have been predictions that the Trump administration won't only expand the export ban but in addition ban cloud use and even access to large language models (LLM) that exceed certain parameters in an...

Create Your Own Nested Sampling Algorithm for Bayesian Parameter Fitting and Model Selection (With Python) Level Up Coding

In today’s recreational coding exercise, we learn a more advanced and robust Monte Carlo approach for model parameter fitting, which also allows us to calculate the Bayesian evidence of a model and perform model...

Create Your Own Nested Sampling Algorithm for Bayesian Parameter Fitting and Model Selection (With Python) Level Up Coding

In today’s recreational coding exercise, we learn a more advanced and robust Monte Carlo approach for model parameter fitting, which also allows us to calculate the Bayesian evidence of a model and perform model...

Cerebras, self-developed small language model released as open source

Artificial intelligence (AI) chip developer Cerebras has released seven small language models trained by itself supercomputer 'Andromeda' as an open source. The language model released by the corporate totally free is a man-made intelligence model...

Conformal prediction for regression The information The workflow Data processing Training and calibration Conformal prediction Predictions quality estimation Optimizing normalization sensitivity parameter beta Optimizing error rate “Easy” approach Conclusion References

I also prepared the “easy” implementation of conformal prediction for regression. As within the previous post the simplicity means going without loops for training multiple models and obtaining multiple calibration tables. Also there isn't...

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