Ornith-1.0-35B - Q4NX for FastFlowLM (AMD Ryzen AI XDNA2)
Ornith-1.0-35B is converted to Q4NX for hardware-accelerated inference with FastFlowLM on AMD Ryzen AI NPUs.
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 >= 0.9.46 (
flm
CLI)
AMD Ryzen AI processor with
XDNA2 (NPU2)
- Strix Point / Ryzen AI 300
series or later
Linux with the XRT NPU stack installed
~47 GB of unified system memory (Q4NX weights + activations + KV cache)
Files
File
Purpose
model.q4nx
Quantized Q4NX text weights
config.json
FastFlowLM model configuration
tokenizer.json
Tokenizer
tokenizer_config.json
Special tokens and chat template
chat_template.jinja
Chat template (optional)
README.md
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
qwen3.6-moe:35b-a3b
. 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"
python3 ./flm-add.py Atomic-Germ/Ornith-1.0-35B-NPU2 --family qwen3.6-moe
Kernels
FastFlowLM's NPU kernels (xclbins) are closed source and are not shipped in this repository. This model uses the
qwen3.6-moe
engine family and is shape-identical to the official
qwen3.6-moe:35b-a3b
model (
Qwen3.6-Moe-35BA3B-NPU2
). Point the runtime's xclbin path at the matching
xclbins
directory (or ship your own) before running.
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