Weekly tutorials and news on adapting large language models (LLMs) to your tasks and hardware using the most recent techniques and models. The Kaitchup proposes a collection of 180+ AI notebooks regularly updated.
| Platform |  | Pricing | Freemium | Publishes | Twice weekly |
|---|---|---|---|---|---|
| Issues | 441 | Founded | 3 years ago | Last Issue | 8 days ago |
| Active | |||||
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Qwen3.6 27B Quantization: FP8 vs INT4 vs NVFP4
Testing accuracy, latency, memory usage, and MTP efficiency after quantization.
Like I did for Qwen3.5 and Gemma 4, let’s see how well Qwen3.6 27B holds up when quantized into different formats: FP8, INT4, and NVFP4.
MTP Layers for Gemma 4 and My Projects in Progress
Hi everyone,
In this edition of The Weekly Kaitchup, let’s discuss:
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The new MTP layers of Gemma 4
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My Projects in Progress
Earlier this week, I published my full comparison of Gemma 4 vs. Qwen 3.5/3.6. One area where Gemma 4 w...
Qwen3.6 27B vs Qwen3.5 27B vs Gemma 4 31B: Accuracy, Latency, Memory, and Token Efficiency Tested
Qwen3.6 improves on Qwen3.5, but Gemma 4 remains surprisingly competitive.
In a previous article, I found Gemma 4 31B to be superior or comparable to Qwen3.5 27B in most areas, with similar or better accuracy and lower latency.
Nemotron 3 Omni Explained: Architecture, Training, and How to Run It
Hi everyone,
In this edition of The Weekly Kaitchup, I discuss Nemotron 3 Omni, a model released this week by NVIDIA.
About my Qwen3.6 27B evaluations
I’m still analyzing the results and hope to publish them on Monday or Tuesday....
How to Reduce LLM Inference Cost and Improve Accuracy with Pass@k and Majority Voting
Is thinking disabled + multiple retries better and still more efficient than thinking enabled?
Ask the same model the same question twice, and you may get two different answers.
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Research scientist in NLP/AI.
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