Instructions to use Kwokou/Spyra-20B-v.1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kwokou/Spyra-20B-v.1.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Kwokou/Spyra-20B-v.1.1-GGUF",
	filename="Spyra-20B-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Kwokou/Spyra-20B-v.1.1-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 Kwokou/Spyra-20B-v.1.1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

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 Kwokou/Spyra-20B-v.1.1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

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 Kwokou/Spyra-20B-v.1.1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

Use Docker

docker model run hf.co/Kwokou/Spyra-20B-v.1.1-GGUF:F16

LM Studio
Jan

vLLM

How to use Kwokou/Spyra-20B-v.1.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kwokou/Spyra-20B-v.1.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kwokou/Spyra-20B-v.1.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kwokou/Spyra-20B-v.1.1-GGUF:F16

Ollama
How to use Kwokou/Spyra-20B-v.1.1-GGUF with Ollama:
```
ollama run hf.co/Kwokou/Spyra-20B-v.1.1-GGUF:F16
```

Unsloth Studio

How to use Kwokou/Spyra-20B-v.1.1-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 Kwokou/Spyra-20B-v.1.1-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 Kwokou/Spyra-20B-v.1.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Kwokou/Spyra-20B-v.1.1-GGUF to start chatting

How to use Kwokou/Spyra-20B-v.1.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

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": "Kwokou/Spyra-20B-v.1.1-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Kwokou/Spyra-20B-v.1.1-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 Kwokou/Spyra-20B-v.1.1-GGUF:F16

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 Kwokou/Spyra-20B-v.1.1-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Kwokou/Spyra-20B-v.1.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Kwokou/Spyra-20B-v.1.1-GGUF:F16

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 "Kwokou/Spyra-20B-v.1.1-GGUF:F16" \
  --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 Kwokou/Spyra-20B-v.1.1-GGUF with Docker Model Runner:
```
docker model run hf.co/Kwokou/Spyra-20B-v.1.1-GGUF:F16
```

Lemonade

How to use Kwokou/Spyra-20B-v.1.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Kwokou/Spyra-20B-v.1.1-GGUF:F16

Run and chat with the model

lemonade run user.Spyra-20B-v.1.1-GGUF-F16

List all available models

lemonade list

Spyra-20B — Domain-Specific LLM for Architectural Design Reasoning (FP16 GGUF)

Spyra-20B is a domain-specific Large Language Model for the AEC industry (Architecture, Engineering, Construction). It combines Tree-of-Thought (ToT) and Chain-of-Thought (CoT) reasoning to decompose complex architectural design problems—mirroring how experienced architects explore alternatives and make decisions.

This version is provided in GGUF format (FP16 precision), optimized for use with Ollama, llama.cpp, or LM Studio.

Model Details

Model Description

Spyra-20B was developed to bridge the gap between general-purpose LLMs and the multidimensional reasoning required in architectural design. The model balances creativity, building code compliance, sustainability, structural constraints, and functional zoning.

The model utilizes a two-channel architecture: an Analysis Channel for structured reasoning (ToT/CoT) and a Final Channel for the user-facing answer.

Developed by: [Nik Ansre & Yara Hirsekorn / Jade Hochschule]
Model type: Causal Language Model (GGUF Quantization)
Language(s): German (primary), English
License: Apache License 2.0
Base model: unsloth/gpt-oss-20b-BF16
Format: GGUF (FP16 High Precision)
Architecture: Mixture-of-Experts (MoE), 20B parameters

Intended Use

Primary Use Cases

Spyra-20B is designed as a professional assistant for architects, urban planners, and AEC professionals:

Design Logic: Parametric design, spatial planning, and zoning strategies.
Urban Densification: Evaluating strategies such as extensions, additions, or courtyard developments with trade-off analysis.
Permit Processes: Navigating building codes (e.g., German BauO) and regulatory compliance logic.
Renovation & Heritage: Handling historic preservation constraints and archaeological findings.
Process Logic: Construction management, stakeholder coordination, and phased planning.

Training Details

Training Procedure

The model was fine-tuned using QLoRA on an expert-curated dataset and subsequently exported to the GGUF format with FP16 precision to ensure maximum reasoning quality and numerical stability compared to lower-bit quantizations.

Training Hyperparameters (Fine-tuning Phase)

Parameter	Value
Base model	unsloth/gpt-oss-20b-BF16
Method	QLoRA (Fine-tuning) -> GGUF Export
Precision	FP16 (GGUF)
Max steps	3,500
Training regime	bf16 mixed precision

How to Use

With Ollama

Create a file named Modelfile (content below).
Ensure the file Spyra-20B-f16.gguf is in the same directory.
Run the following commands:

ollama create spyra-20b -f Modelfile
ollama run spyra-20b

Downloads last month: 22

GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

16-bit

Model tree for Kwokou/Spyra-20B-v.1.1-GGUF

Base model

openai/gpt-oss-20b

Finetuned

unsloth/gpt-oss-20b-BF16

Quantized

(13)

this model