antiableofnormies/qwen3.5-4b-lora-android-dev
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Code Style LoRA for Qwen3.5-4B
A QLoRA adapter fine-tuned on Android project source files to teach Qwen3.5-4B a specific coding style. Designed for llama.cpp (via GGUF conversion) or HuggingFace Transformers + PEFT.
What this adapter does
- Learns Kotlin coding patterns: naming conventions, import ordering, brace style, comment density
- Captures Android architecture patterns: ViewModel, Repository, Room DB, Retrofit networking
- Embeds dependency injection style (Hilt)
- No unwanted boilerplate — trained to generate exactly the level of verbosity you use
Training details
Usage with Transformers + PEFT
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
model = PeftModel.from_pretrained(
AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-4B", device_map="auto"),
"antiableofnormies/qwen3.5-4b-lora-android-dev",
)
tokenizer = AutoTokenizer.from_pretrained("antiableofnormies/qwen3.5-4b-lora-android-dev")Usage with llama.cpp
python convert_lora_to_gguf.py --base Qwen3.5-4B-Q4_K_M.gguf --lora ./qwen3.5-4b-style-lora/ --output style-adapter.gguf
llama-server -m Qwen3.5-4B-Q4_K_M.gguf --lora style-adapter.gguf --host 0.0.0.0 -ngl 99 --ctx-size 32768 --port 8080 --mlock
# Optional: merge into a standalone GGUF
llama-export-lora -m Qwen3.5-4B-Q4_K_M.gguf --lora style-adapter.gguf -o qwen3.5-4b-code-style-merged.ggufIntended use
This adapter is designed for a local coding assistant (opencode + llama.cpp) that:
- Runs entirely on your machine (no API calls)
- Respects your existing code conventions
- Avoids unwanted boilerplate that cloud APIs tend to add
- Matches the patterns found in the training projects
Limitations
- Trained on a personal codebase — style may not generalize to unrelated projects
- LoRA rank 16 captures high-level style (naming, structure) but not deep domain knowledge
- Jetpack Compose UI code was explicitly excluded from training
- English-only code and comments
