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 / README.md

RahnTechLabs

Release standalone rtl-flutter-0.2 Q4_K_M

168c4c6 verified about 1 month ago

preview code

Raw History Blame Contribute Delete

2.48 kB

	---
	base_model: Qwen/Qwen3.5-4B
	license: apache-2.0
	library_name: llama.cpp
	pipeline_tag: text-generation
	tags:
	- flutter
	- dart
	- mobile-development
	- gguf
	- llama.cpp
	---

	# RTL-Flutter 0.2

	`rtl-flutter-0.2` is a standalone, merged GGUF model for Dart, Flutter, and
	mobile-engineering assistance. The LoRA adapter has already been merged into
	the Qwen3.5-4B base; users do not need to download or pass a separate adapter.

	## Run with llama.cpp

	Download this repository and use the GGUF file directly:

	```bash
	hf download RahnTechLabs/rtl-flutter-0.2 \
	rtl-flutter-0.2.gguf --local-dir ./rtl-flutter-0.2

	llama-cli \
	-m ./rtl-flutter-0.2/rtl-flutter-0.2.gguf \
	--jinja \
	--reasoning-budget 0 \
	-p "Explain how Flutter Widget.canUpdate works."
	```

	For a local OpenAI-compatible server:

	```bash
	llama-server \
	-m ./rtl-flutter-0.2/rtl-flutter-0.2.gguf \
	--jinja \
	--reasoning-budget 0
	```

	The file is Q4_K_M quantized and is approximately 2.7 GB. A llama.cpp build
	with Qwen3.5 support is required; current llama.cpp releases provide this
	architecture.

	## Intended use and limitations

	This is an experimental domain model for engineering assistance, code review,
	debugging explanations, and architecture discussions involving Flutter, Dart,
	Android, and iOS. It can produce confident errors, especially on version-
	specific APIs and edge cases. Verify answers against the current SDK and
	official documentation before shipping production code.

	The held-out benchmark and training data are not included in this repository.
	Do not put secrets, proprietary code, or personal data into prompts.

	## Training and provenance

	- Base: `Qwen/Qwen3.5-4B`
	- 475 training examples and 25 validation examples
	- 2 epochs, learning rate `5e-6`
	- LoRA rank 8, alpha 16, dropout 0.05
	- bfloat16 training in the project ROCm/PyTorch workflow
	- Sources included authorized local Dart/Flutter material and current official
	Flutter and `flutter_bloc` documentation

	The published file was produced by merging the project LoRA adapter into a
	compatible Q4_K_M base, then requantizing the merged weights to Q4_K_M for
	standalone distribution. Requantization can cause a small quality change from
	the unquantized merged intermediate.

	## License

	The base model is distributed under Apache-2.0. This release contains derived
	weights, so review the base model terms and ensure that you have the necessary
	rights for any local source material before redistributing or deploying it.