A QLoRA adapter fine-tuned on 201 Android project source files to teach Qwen3.5-9B 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

Parameter	Value
Base model	Qwen/Qwen3.5-9B
Method	QLoRA (4-bit NF4 via Unsloth)
LoRA rank	r=16, alpha=16
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training data	201 source files from 2 Android projects
File types	Kotlin (169), Gradle KTS (18), XML resources (14)
Epochs	3
Sequence length	2048
Learning rate	2e-4 (cosine scheduler)
Hardware	T4 GPU (16GB VRAM) via Google Colab
Framework	Unsloth + TRL SFTTrainer

Usage with Transformers + PEFT

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model = PeftModel.from_pretrained(
    AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", device_map="auto"),
    "antiableofnormies/qwen3.5-9b-lora-android-dev",
)
tokenizer = AutoTokenizer.from_pretrained("antiableofnormies/qwen3.5-9b-lora-android-dev")

Usage with llama.cpp

bash

python convert_lora_to_gguf.py     --base Qwen3.5-9B-Q4_K_M.gguf     --lora ./qwen3.5-style-lora/     --output style-adapter.gguf

llama-server -m Qwen3.5-9B-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 Qwen3.5-9B-Q4_K_M.gguf     --lora style-adapter.gguf     -o qwen3.5-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