AtomicChat/Qwen3.5-9B-DFlash-GGUF

Base model: z-lab/Qwen3.5-9B-DFlash

Qwen3.5 9B Dflash, self-quantized to GGUF by Atomic Chat. Built straight from Z Lab's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

1.3B parameters: the weights this repo quantizes.
Context length: 262,144 tokens (256K), as published by Z Lab.
6 layers: Dense decoder, hybrid sliding-window (4096) and global attention.
Full imatrix ladder: every quant is calibrated with an importance matrix.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the Qwen3.5 9B Dflash chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`z-lab/Qwen3.5-9B-DFlash`
Parameters	1.3B
Layers	6
Sliding window	4096 tokens
Context length	262,144 tokens (256K)
Vocabulary	248,320
Modalities	Text
Architecture	Dense decoder, hybrid sliding-window (4096) and global attention, 32 attention heads over 8 KV heads, `DFlashDraftModel`
This repo	GGUF quants (imatrix). Quants: `Q8_0`

Choosing a quant

Quant	Size	Notes
`Q8_0`	1.4 GB	Effectively lossless, reference quality.

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q8_0 is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Qwen3.5 9B Dflash locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/Qwen3.5-9B-DFlash-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/Qwen3.5-9B-DFlash-GGUF:Q8_0 --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/Qwen3.5-9B-DFlash-GGUF:Q8_0
LM Studio / Jan: search the repo id, download any quant.

Best practices

Parameter	Value
sampling defaults	not stated

The base model card does not state sampling defaults.

Run in llama.cpp

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/Qwen3.5-9B-DFlash-GGUF:Q8_0 \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

Download z-lab/Qwen3.5-9B-DFlash (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over our calibration corpus.
Quantize the ladder with --imatrix.

License

Original model by Z Lab, released under the Apache 2.0 license. Quantized by Atomic Chat.