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 | 449 | Founded | 3 years ago | Last Issue | 14 hours ago |
| Active | |||||
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Make Your Own Optimized GGUFs with AutoRound
Build optimized GGUF models for llama.cpp and LM Studio using AutoScheme, custom bit-widths, and layer protection.
GGUF is the standard format for running LLMs locally with tools such as llama.cpp and LM Studio. It stores both model weight...
MoQ GGUFs and GSQ: Low-Bit GGUFs Are About to Get Much Better
Hi everyone,
In this edition of The Weekly Kaitchup, let’s discuss:
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My first MoQ GGUFs and what’s next
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The Nemotron 3 Ultra released by NVIDIA
30% Off the Subscription to The Kaitchup:
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DFlash vs MTP: Qwen3.6 Speculative Decoding Benchmarks with vLLM and llama.cpp
Up to 4x faster inference -- Benchmarking the speed on various task on coding, chat, and math tasks, with optimal hyperparameters
Qwen3.6 inference is faster when MTP layers are enabled to draft tokens. Gemma 4 now supports this option as...
Qwen3.5 9B MoQ: Inside a Strong 3.6-bit GGUF
Hi everyone,
In this edition of The Weekly Kaitchup, let’s discuss:
-
Mixture of Quantization (MoQ) GGUFs
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LFM2.5 8B A1B: 38T Training Tokens for 1.5B Active Parameters
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TokenSpeed
30% Off the Subscription to The Kaitc...
Reasoning Budgets vs. Structured CoT: Controlling LLM Thinking Tokens
Evaluations of BNF grammars and reasoning budgets with Qwen3.6 27B
LLMs now rely heavily on reasoning traces to improve accuracy. A reasoning trace is the intermediate text generated before the final answer, often delimited by tags such as...
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Research scientist in NLP/AI.
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