Instructions to use qgfvadfuvads/Q-Prefer-D2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use qgfvadfuvads/Q-Prefer-D2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct") model = PeftModel.from_pretrained(base_model, "qgfvadfuvads/Q-Prefer-D2") - Notebooks
- Google Colab
- Kaggle
| name: ci | |
| on: | |
| push: | |
| pull_request: | |
| jobs: | |
| lightweight-tests: | |
| runs-on: ubuntu-latest | |
| steps: | |
| - uses: actions/checkout@v4 | |
| - uses: actions/setup-python@v5 | |
| with: | |
| python-version: "3.10" | |
| cache: pip | |
| - name: Install CPU dependencies | |
| run: | | |
| python -m pip install --upgrade pip | |
| python -m pip install --index-url https://download.pytorch.org/whl/cpu torch==2.8.0 | |
| python -m pip install -e ".[dev]" | |
| - name: Unit tests | |
| run: python -m unittest discover -s tests -v | |
| - name: Compile check | |
| run: python -m compileall -q src examples scripts tests training validation | |
| - name: Ruff | |
| run: ruff check src examples scripts tests validation | |