qwen3-8b-lora-android-dev

Code Style LoRA for Qwen3-8B

A QLoRA adapter fine-tuned on 201 Android project source files to teach Qwen3-8B 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("Qwen/Qwen3-8B", device_map="auto"),
    "antiableofnormies/qwen3-8b-lora-android-dev",
)
tokenizer = AutoTokenizer.from_pretrained("antiableofnormies/qwen3-8b-lora-android-dev")
Usage with llama.cpp
python convert_lora_to_gguf.py     --base Qwen3-8B-Q4_K_M.gguf     --lora ./qwen3-style-lora/     --output style-adapter.gguf

llama-server -m Qwen3-8B-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 Qwen3-8B-Q4_K_M.gguf     --lora style-adapter.gguf     -o qwen3-code-style-merged.gguf
Intended 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