---
license: apache-2.0
language:
- en
base_model:
- tomg-group-umd/DynaGuard-4B
base_model_relation: quantized
quantized_by: Atomic-Germ
pipeline_tag: text-generation
tags:
- guardrail
- safety
- moderation
- dynaguard
- umd
- qwen3
- llm
- fastflowlm
- q4nx
- npu2
---
# *IF YOU USE COMMUNITY QWEN MODELS DO NOT UPGRADE TO FLM v1.0.2+*

# 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](https://fastflowlm.com)
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`

```bash
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.

## Model

- Registry tag: `dynaguard:4b`
- Engine family: `qwen3`
- Kernel source: Qwen3-4B-NPU2
- Context length: 262,144 tokens (from config)
- `model.q4nx` size: 3.3 GB
- Base model: [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
- License: apache-2.0



## Original model card

See the upstream model card for training details, benchmarks, and upstream
usage. This repository only contains the Q4NX conversion for FastFlowLM.
- Upstream card: [tomg-group-umd/DynaGuard-4B(https://huggingface.co/tomg-group-umd/DynaGuard-4B)