Qwen3.5-9B-DeepSeek-V4-Flash-GGUF Brings Deep Reasoning Home

The Qwen3.5-9B-DeepSeek-V4-Flash-GGUF is a compressed language model that packs DeepSeek-V4’s advanced reasoning into a 9-billion-parameter package for local use. It converts the full model into the GGUF format, so it runs efficiently on common consumer graphics cards without cloud servers. Users get strong multi-step logic, math, and coding skills in a compact file that saves memory and speeds up inference.

Jackrong who also worked on Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled and Qwopus3.6-27B-v1-preview-GGUF distilled this model from a curated 8000-sample dataset with help from hardware engineer Kyle Hessling, who managed testing and compute. The project aimed to transfer genuine reasoning procedures, not just the teacher’s output style, from a trillion-parameter model to a 9B one. The GGUF release makes it useful for privacy-minded pros, small teams, and hobbyists running AI on their own hardware.

Fast reasoning on everyday hardware

Home-lab users can run advanced reasoning offline, keeping personal or client data fully private. Small agencies get a low-cost tool for drafting code and automating document workflows without monthly fees. Privacy-sensitive roles such as legal, medical, or finance can now run complex analysis locally, never sending data out.

Training notes and known limits

Training on an NVIDIA DGX used Unsloth to teach genuine procedures, not superficial style copying. The model sometimes over-reasons on simple queries—a known quirk of its logic bias. Recommended settings: temperature 0.7–1.0, top_p 0.95, ChatML prompt.

"By leveraging the dataset Jackrong/DeepSeek-V4-Distill-8000x, this model successfully transfers the advanced structured reasoning and multi-step problem-solving capabilities of the DeepSeek-V4 architecture into the highly efficient Qwen3.5-9B parameter space." — Source: Hugging Face