GGUF quantizations of Ailiance-fr/SchGen-Qwen3.6-27B-EU — a sovereign EU KiCad schematic-generation model (QLoRA fine-tune of Qwen/Qwen3.6-27B, SchGen method, Luo et al., 2026) — for llama.cpp and Ollama.

Files

File	Quant	Size
`SchGen-Qwen3.6-27B-EU-Q4_K_M.gguf`	Q4_K_M	~16.5 GB
`SchGen-Qwen3.6-27B-EU-Q8_0.gguf`	Q8_0	~28 GB

⚠️ Runtime requirement (read this)

The base architecture is qwen3_5 (Qwen3.6, hybrid linear/state-space attention) — a recent arch. You need a recent llama.cpp / Ollama that registers LLM_ARCH_QWEN35. Verified: Ollama 0.19.0 loads and runs it. Older runtimes fail at load with "unknown model architecture". On CPU the 27B is slow; use a GPU for usable throughput.

Usage — Ollama

bash

# pull the file, then:
cat > Modelfile <<EOF
FROM ./SchGen-Qwen3.6-27B-EU-Q4_K_M.gguf
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0
EOF
ollama create schgen -f Modelfile
ollama run schgen "Generate a KiCad schematic for an RC low-pass filter, 1kHz cutoff."

Usage — llama.cpp

bash

llama-cli -m SchGen-Qwen3.6-27B-EU-Q4_K_M.gguf \
  -p "Generate a KiCad schematic with an LM358 inverting amplifier, gain -10." \
  -n 2048 --temp 0

The model emits executable Python in a schematic DSL (4 primitives: add_schematic_symbol, get_pin_location, add_label, connect_pins + write_out_all_wires()). Run with thinking disabled. Output must be checked with KiCad ERC/DRC — it is an assistant, not autonomous. See the main model card for evaluation, limitations and citation.

License

Apache-2.0 (see NOTICE). Derivative of Qwen/Qwen3.6-27B (Apache-2.0), trained on microsoft/SchGen_dataset (MIT); method = SchGen (MIT).

Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF

SchGen-Qwen3.6-27B-EU — GGUF

Files

⚠️ Runtime requirement (read this)

Usage — Ollama

Usage — llama.cpp

License

Ailiance-fr/
SchGen-Qwen3.6-27B-EU-GGUF