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
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