DynaGuard-4B - Q4NX for FastFlowLM (AMD Ryzen AI XDNA2)

A safety-focused Qwen3-4B fine-tune, converted to Q4NX for FastFlowLM.

What is Q4NX?

Q4NX is FastFlowLM's native packed-quantization format - a rearranged Q4_1 layout tuned for the NPU matrix engine's tile sizes and memory access patterns. It is not a GGUF file and it does not run on llama.cpp or Ollama; it is meant exclusively for the FastFlowLM engine on AMD Ryzen AI NPUs.

Requirements

FastFlowLM >= 0.9.45 (flm CLI)
AMD Ryzen AI processor with XDNA2 (NPU2) - Strix Point / Ryzen AI 300 series or later
Linux with the XRT NPU stack installed
~16 GB of unified system memory (Q4NX weights + activations + KV cache)

FLM Bench

Tested on an AMD Ryzen AI 340 Framework 13 laptop.

Context Length	TTFT (s) (mean ± std)	Prefill Speed (tok/s) (mean ± std)	Decoding Speed (tok/s) (mean ± std)
1k	2.681 ± 0.080	364.63 ± 10.92	13.20 ± 0.01
2k	4.435 ± 0.124	438.85 ± 12.27	12.11 ± 0.38
4k	8.306 ± 0.082	467.13 ± 4.65	11.27 ± 0.22
8k	17.121 ± 0.001	452.58 ± 0.02	9.83 ± 0.00
16k	41.227 ± 0.001	375.64 ± 0.00	7.58 ± 0.00
32k	115.272 ± 0.024	268.61 ± 0.06	5.22 ± 0.00

Files

File	Purpose
model.q4nx	Quantized Q4NX weights
config.json	FastFlowLM model configuration
tokenizer.json	Tokenizer
tokenizer_config.json	Special tokens and chat template
chat_template.jinja	Chat template (optional)
flm-add.py	Installer script - registers this model with FastFlowLM

Install and run

This repository works with flm-add, a small installer that copies the model into the FastFlowLM user directory and registers the tag. It never modifies the system FastFlowLM install.

pip install flm-add or uv tool install flm-add

uv tool install flm-add
flm-add Atomic-Germ/DynaGuard-4B-NPU2 --tag dynaguard:4b --family qwen3
FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json" FLM_XCLBIN_PATH="$HOME/.config/flm" flm run dynaguard:4b

Kernels

FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. flm-add links the kernels of the official qwen3:4b model (Qwen3-9B-NPU2), because this model shares the same engine family (qwen3) and architecture.