prithivMLmods/Qwen3-4B-Thinking-2507-DAG-GGUF

Qwen3-4B-Thinking-2507-DAG-GGUF

The sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning model is an experimental specialist reasoning AI finetuned for multi-step causal analysis and reasoning, producing structured Directed Acyclic Graphs (DAGs) in response to user input across fields like programming, science, business, law, and more; it structures its output in a readable JSON graph format with confidence measures, enabling easy visualization and further analysis, and supports prompt-driven graph generation using a custom Qwen3-4B-Thinking-2507 inference approach, making it suitable for advanced reasoning tasks on both desktop and server environments; the model is part of open-source research, benefits from a custom DAG dataset, and is recommended for those needing clear causal structure in analysis or automation outputs.

Model Files

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):