Instructions to use witflag/Clarion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use witflag/Clarion 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 witflag/Clarion:Q4_K_M # Run inference directly in the terminal: llama cli -hf witflag/Clarion:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf witflag/Clarion:Q4_K_M # Run inference directly in the terminal: llama cli -hf witflag/Clarion: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 witflag/Clarion:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf witflag/Clarion: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 witflag/Clarion:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf witflag/Clarion:Q4_K_M
Use Docker
docker model run hf.co/witflag/Clarion:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use witflag/Clarion with Ollama:
ollama run hf.co/witflag/Clarion:Q4_K_M
- Unsloth Studio
How to use witflag/Clarion 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 witflag/Clarion 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 witflag/Clarion to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for witflag/Clarion to start chatting
- Pi
How to use witflag/Clarion with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Clarion: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": "witflag/Clarion:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use witflag/Clarion with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Clarion: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 witflag/Clarion:Q4_K_M
Run Hermes
hermes
- OpenClaw new
How to use witflag/Clarion with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Clarion: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 "witflag/Clarion: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 witflag/Clarion with Docker Model Runner:
docker model run hf.co/witflag/Clarion:Q4_K_M
- Lemonade
How to use witflag/Clarion with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull witflag/Clarion:Q4_K_M
Run and chat with the model
lemonade run user.Clarion-Q4_K_M
List all available models
lemonade list
- Atomic Chat
| datasets: | |
| - TeichAI/claude-sonnet-4.5-high-reasoning-250x | |
| base_model: | |
| - unsloth/Qwen3-14B-unsloth-bnb-4bit | |
| license: apache-2.0 | |
| # Qwen3 14B Claude Sonnet 4.5 Reasoning Distill | |
| This model was trained on a **Claude Sonnet 4.5 (reasoning)** dataset with a high reasoning effort. | |
| - 🤖 Related Models: | |
| | Model | Effective parameters | Active parameters | | |
| | ------------- | ------------- | ------------- | | |
| | [`TeichAI/TeichAI/Qwen3-30B-A3B-Thinking-2507-Claude-4.5-Sonnet-High-Reasoning-Distill-GGUF`](https://huggingface.co/TeichAI/Qwen3-30B-A3B-Thinking-2507-Claude-4.5-Sonnet-High-Reasoning-Distill-GGUF) | 30 B | 3 B | | |
| | [`TeichAI/gpt-oss-20b-claude-4.5-sonnet-high-reasoning-distill-GGUF`](https://huggingface.co/TeichAI/gpt-oss-20b-claude-4.5-sonnet-high-reasoning-distill-GGUF) | 20 B | 3 B | | |
| | [`TeichAI/Qwen3-8B-Claude-Sonnet-4.5-Reasoning-Distill-GGUF`](https://huggingface.co/TeichAI/Qwen3-8B-Claude-Sonnet-4.5-Reasoning-Distill-GGUF) | 8 B | 8 B | | |
| - 🧬 Datasets: | |
| - `TeichAI/claude-sonnet-4.5-high-reasoning-250x` | |
| - 🏗 Base Model: | |
| - `unsloth/Qwen3-14B-unsloth-bnb-4bit` | |
| - ⚡ Use cases: | |
| - Coding | |
| - Science |