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 | 457 | Founded | 3 years ago | Last Issue | 8 days ago |
| Active | |||||
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kaitchup.substack.comLatest Issues
Efficient and Reasoning AI at the ACL 2026
Hi everyone,
In this edition of The Weekly Kaitchup, I’ll highlight some of the most interesting work I saw at ACL 2026, which I attended this week in San Diego.
I’ll also discuss OpenAI’s recent take on benchmarking with SWE-Bench Pro, o...
LFM2.5 230M and 350M: How Accurate Are the GGUF Versions?
230M or 350M GGUFs?
Tiny language models that can run quickly with very limited memory are one of Liquid AI’s specialties. And while models with only a few hundred million parameters obviously cannot rival today’s multi-billion-parameter m...
DSpark and NVIDIA's Qwen3.6 NVFP4 Models
Hi everyone,
In this edition of The Weekly Kaitchup:
-
Which NVFP4 Version of Qwen3.6 27B Should You Use?
-
DSpark: DeepSeek’s New Speculative Decoding Method
I’m at the ACL 2026 and stopped by the Qwen corner to ask the importan...
MiniMax M3 GGUF Quantization: From 852 GB to ~150 GB Without Breaking Accuracy
Benchmarks, token efficiency, and tensor-level analysis of low-bit M3 GGUFs.
MiniMax M3 is an excellent model, but with roughly 428B parameters, running it locally is very challenging. In BF16, the weights alone require around 852 GB of me...
This Week in Open Models: Tiny LFM2.5, Ornith-1.0, and GLM-5.2 REAP
Hi everyone,
In this edition of The Weekly Kaitchup, let’s discuss some of the open models released this week:
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LFM2.5 230M: Tiny Models Can Follow Instructions
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Ornith-1.0: Self-Scaffolding Coding Models
-
GLM-5.2 REAP: Cutting...
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Research scientist in NLP/AI.
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