Instructions to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Ollama:
```
ollama run hf.co/Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
```

Unsloth Studio

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF to start chatting

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Docker Model Runner:
```
docker model run hf.co/Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M
```

Lemonade

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SchGen-Qwen3.6-27B-EU-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

SchGen-Qwen3.6-27B-EU — GGUF

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

# 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

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).

Downloads last month: 80

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

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

Base model

Qwen/Qwen3.6-27B

Finetuned

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

Quantized

(3)

this model

Dataset used to train Ailiance-fr/SchGen-Qwen3.6-27B-EU-GGUF

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

SchGen: PCB Schematic Generation with Semantic-Grounded Code Representations

Paper • 2605.30345 • Published May 28 • 1