How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Atomic-Germ/Qwen3.8-Distilled-2B-NPU2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Atomic-Germ/Qwen3.8-Distilled-2B-NPU2", device_map="auto")
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Qwen3.8-Distilled-2B-NPU2-NPU2

OpenFlowLM Q4NX conversion of empero-ai/Qwen3.8-2B-Distill for AMD XDNA NPU inference.

This repository contains a quantized Q4NX port of the model, compiled for the OpenFlowLM (OFLM) runtime. It is not a GGUF file.

Item Value
Source model Aempero-ai/Qwen3.8-2B-Distill
Source GGUF Qwen3.8-2B-Distill-Q8_0.gguf
Weights model.q4nx (2.29 GB)
Modality language / vision
OFLM version 0.1.0
Converted 2026-09-27

Install and run

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

pip install oflm-add or uv tool install oflm-add

uv tool install oflm-add
oflm-add Atomic-Germ/Qwen3.8-Distilled-2B-NPU2-NPU2 --family qwen3.5 --xclbin-from Qwen3.8-Distilled-2B-NPU2-NPU2
OFLM_CONFIG_PATH="$HOME/.config/oflm/model_list.json" OFLM_XCLBIN_PATH="$HOME/.config/oflm" oflm run Qwen3.8-Distilled-2B-NPU2-NPU2

Files

File Description
model.q4nx Quantized weights (Q8_0 / Q4_1 / BF16)
config.json OFLM runtime configuration
tokenizer.json Tokenizer vocabulary
tokenizer_config.json Tokenizer configuration
chat_template.jinja Chat template
vision_weight.q4nx Vision model

Source model card

See the original model card: Atomic-Germ/Qwen3.8-2B-Distill-NPU2

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