Instructions to use RahnTechLabs/rtl-flutter-0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use RahnTechLabs/rtl-flutter-0.2 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 RahnTechLabs/rtl-flutter-0.2
# Run inference directly in the terminal:
llama cli -hf RahnTechLabs/rtl-flutter-0.2

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RahnTechLabs/rtl-flutter-0.2
# Run inference directly in the terminal:
llama cli -hf RahnTechLabs/rtl-flutter-0.2

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 RahnTechLabs/rtl-flutter-0.2
# Run inference directly in the terminal:
./llama-cli -hf RahnTechLabs/rtl-flutter-0.2

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 RahnTechLabs/rtl-flutter-0.2
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RahnTechLabs/rtl-flutter-0.2

Use Docker

docker model run hf.co/RahnTechLabs/rtl-flutter-0.2

LM Studio
Jan

vLLM

How to use RahnTechLabs/rtl-flutter-0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RahnTechLabs/rtl-flutter-0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RahnTechLabs/rtl-flutter-0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RahnTechLabs/rtl-flutter-0.2

Ollama
How to use RahnTechLabs/rtl-flutter-0.2 with Ollama:
```
ollama run hf.co/RahnTechLabs/rtl-flutter-0.2
```
Unsloth Desktop

How to use RahnTechLabs/rtl-flutter-0.2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RahnTechLabs/rtl-flutter-0.2

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RahnTechLabs/rtl-flutter-0.2"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RahnTechLabs/rtl-flutter-0.2 with Docker Model Runner:
```
docker model run hf.co/RahnTechLabs/rtl-flutter-0.2
```

Lemonade

How to use RahnTechLabs/rtl-flutter-0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RahnTechLabs/rtl-flutter-0.2

Run and chat with the model

lemonade run user.rtl-flutter-0.2-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use RahnTechLabs/rtl-flutter-0.2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RahnTechLabs/rtl-flutter-0.2

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 RahnTechLabs/rtl-flutter-0.2

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RahnTechLabs/rtl-flutter-0.2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RahnTechLabs/rtl-flutter-0.2

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 "RahnTechLabs/rtl-flutter-0.2" \
  --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"

rtl-flutter-0.2

File size: 1,231 Bytes

168c4c6

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  "quantization": "Q4_K_M",
  "standalone": true,
  "base_model": "Qwen/Qwen3.5-4B",
  "license": "apache-2.0",
  "source_adapter": "rtl-flutter-v0.2-r8",
  "training": {
    "train_rows": 475,
    "validation_rows": 25,
    "epochs": 2,
    "learning_rate": 5e-6,
    "lora_rank": 8,
    "lora_alpha": 16,
    "lora_dropout": 0.05,
    "precision": "bfloat16"
  },
  "validation_metrics": {
    "validation_loss": 1.7573,
    "validation_perplexity": 5.797,
    "elapsed_seconds": 1723,
    "peak_vram_gb": 9.96
  },
  "benchmark": {
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    "questions_included": false
  },
  "privacy": {
    "training_data_copied": false,
    "benchmark_questions_copied": false,
    "private_project_paths_copied": false
  },
  "llama_cpp": {
    "file": "rtl-flutter-0.2.gguf",
    "base_quantization": "Q4_K_M",
    "standalone": true,
    "sha256": "8bce1c52c570dc5a63f1c302e62da4fca29805c6cbe961ccb54e21cd54e09fed"
  },
  "files": {
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      "bytes": 2708804768,
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}