Atomic-Germ / DynaGuard-4B-NPU2

huggingface.co
Total runs: 161
24-hour runs: -3
7-day runs: -29
30-day runs: -505
Model's Last Updated: August 20 2026
text-generation

Introduction of DynaGuard-4B-NPU2

Model Details of DynaGuard-4B-NPU2

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)
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 ships flm-add.py , a small installer that copies the model into the FastFlowLM user directory and registers the tag qwen3.5-code:9b . It never modifies the system FastFlowLM install.

# one-time environment (add these to ~/.bashrc)
export FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json"
export FLM_XCLBIN_PATH="$HOME/.config/flm"

git lfs install
git clone https://huggingface.co/Atomic-Germ/DynaGuard-4B-NPU2
cd Qwen3.5-9B-Claude-Code-NPU2
python3 ./flm-add.py . --tag dynaguard:4b
flm run qwen3.5-code:9b

Run python3 ./flm-add.py --help for all options. Without a clone, the same command works against the repo id directly:

python3 ./flm-add.py Atomic-Germ/DynaGuard-4B-NPU2 --tag dynaguard:4b
Kernels

FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. flm-add.py links the kernels of the official qwen3.5:9b model ( Qwen3.5-9B-NPU2 ), because this model shares the same engine family ( qwen3.5 ) and architecture.

Serve (OpenAI-compatible)
flm serve dynaguard:4b --port 8080
curl http://127.0.0.1:8080/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"dynaguard:4b","messages":[{"role":"user","content":"Hello!"}],"max_tokens":256}'
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
  • License: apache-2.0
GhostWriter Influence Test (Arbitrary but repeatable benchmark)

Tested on an AMD Ryzen AI 340 Framework 13 laptop.

Metric Value
Prompt Tokens 11,254
Completion Tokens 1,367
Total Tokens 12,621
Active KV Tokens 12,621
Max KV Token Capacity 32,768
KV Token Occupancy 38.52%
Load Duration 0.000001342 seconds
Prefill Duration (TTFT) 29.90 ms
Decoding Duration 165.02 ms
Prefill Speed 376.31 tokens/sec
Decoding Speed 8.28 tokens/sec

"load_duration":1.342e-06,"prefill_duration_ttft":29.906345984,"decoding_duration":165.0172,"prefill_speed_tps":376.30809213606136,"decoding_speed_tps":8.283984942175724}


Original model card

See the upstream model card for training details, benchmarks, and upstream usage. This repository only contains the Q4NX conversion for FastFlowLM.

Runs of Atomic-Germ DynaGuard-4B-NPU2 on huggingface.co

161
Total runs
-3
24-hour runs
-6
3-day runs
-29
7-day runs
-505
30-day runs

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DynaGuard-4B-NPU2 install

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