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
Choosing a quant
[!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
The base model card does not state sampling defaults.
Run in llama.cpp
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.