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
XRT NPU stack installed
32 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 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/BigBang1.0-35B-A3B-NPU2 --family qwen3.6-moe --tag bigbang1.0-moe:35b-a3b
FLM_XCLBIN_PATH="$HOME/.config/flm" FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json" flm run bigbang1.0-moe:35b-a3b

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 qwen3.6-moe:35b-a3b model (Qwen3.6-35B-A3B-NPU2), because this model shares the same engine family (qwen3.6-moe) and architecture.

Model

Registry tag: bigbang1.0-moe:35b-a3b
Engine family: qwen3.6-moe
Kernel source: Qwen3.6-35B-A3B-NPU2

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: endless-frontier/BigBang-v1