Atomic-Germ / Qwen3.8-Distilled-9B-NPU2

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Model's Last Updated: October 12 2026
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Introduction of Qwen3.8-Distilled-9B-NPU2

Model Details of Qwen3.8-Distilled-9B-NPU2

Qwen3.8 9B Now With Vision

FastFlowLM Q4NX conversion of empero-ai/Qwen3.8-9B for AMD XDNA NPU inference.

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

Item Value
Source model empero-ai/Qwen3.8-9B
Source GGUF Qwen3.8-9B-Q8_0.gguf
Weights model.q4nx (7.23 GB)
Modality language
FLM version 1.0.1
Converted 2026-08-16
Source repository

Metadata from the upstream Hugging Face repository:

Item Value
License apache-2.0
Base model Qwen/Qwen3.5-9B
Library transformers
Model type qwen3_5
Pipeline text-generation
Downloads 128
Repo revision 0934f3d2327ff2df2197495278c4c46ae5a56bd9
Files
File Description
model.q4nx Quantized weights (Q8_0 / Q4_1 / BF16)
config.json FLM runtime configuration
tokenizer.json Tokenizer vocabulary
tokenizer_config.json Tokenizer configuration
chat_template.jinja Chat template
vision_weight.q4nx Vision model
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

uv tool install flm-add
flm-add Atomic-Germ/Qwen3.8-Distilled-9B-NPU2 --tag qwen3.8-distilled:9b --family qwen3.5 --xclbin-from Qwen3.5-9B-NPU2 (with --force to update)
FLM_CONFIG_PATH="$HOME/.config/flm/model_list.json" FLM_XCLBIN_PATH="$HOME/.config/flm" flm run qwen3.8-distilled:9b

Source model card
Qwen3.8-9B

Developed by Empero

This repository contains model weights and configuration files in the Hugging Face Transformers format.

These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.5 architecture support.

Qwen3.8-9B is a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-9B architecture. The student was trained on ~70,000 curated teacher traces from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, code, general reasoning, instruction following, and tool use, quality-filtered before training.

The objective: bring the reasoning behavior of a frontier-scale teacher into a dense 9B that deploys on a single GPU.

Highlights
  • Distilled chain-of-thought — every answer opens with a <think> block learned directly from Qwen3.8 2.4T A95B traces rather than synthetic self-generated reasoning.
  • Mathematics and code emphasis — the trace mix is deliberately weighted toward hard math and competitive programming, the domains where distillation moves the needle most at this scale.
  • Native function calling per Qwen3.5's specification — no wrapper or tool-specific fine-tune required.
  • 262,144-token native context , inherited from the Qwen3.5 base.
  • Full fine-tune — every parameter updated; not an adapter.
Model Overview
  • Type: Causal Language Model (text path of a vision-language base)
  • Base: Qwen/Qwen3.5-9B
  • Number of Parameters: 9B
  • Training: SFT (off-policy distillation) on ~70,000 teacher traces
  • Teacher: Qwen3.8 2.4T A95B (internal distillation datasets)
  • Context Length: 262,144 natively

Provenance & licensing

Weights are released under Apache-2.0 , inherited from the Qwen3.5-9B base. Shared for research and experimentation, as-is.

Acknowledgements

Runs of Atomic-Germ Qwen3.8-Distilled-9B-NPU2 on huggingface.co

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