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:
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:
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_blocdocumentation
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.