Instructions to use emrevrg/AUBIN-E4B-Control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use emrevrg/AUBIN-E4B-Control with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E4B-it") model = PeftModel.from_pretrained(base_model, "emrevrg/AUBIN-E4B-Control") - Notebooks
- Google Colab
- Kaggle
Download code/Dockerfile from emrevrg/AUBIN-E4B-Control: direct link, hf CLI and curl.
- Browser
- Download file 461 Bytes
-
https://huggingface.co/emrevrg/AUBIN-E4B-Control/resolve/main/code/Dockerfile
- Command line
-
hf download hf://emrevrg/AUBIN-E4B-Control/code/Dockerfile
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curl -L -o Dockerfile https://huggingface.co/emrevrg/AUBIN-E4B-Control/resolve/main/code/Dockerfile
461 Bytes
| # AUBIN server (GPU). Build: docker build -t aubin . | |
| # Run: docker run --gpus all -p 8009:8009 -e AUBIN_MODEL=emrevrg/AUBIN-12B aubin | |
| FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime | |
| WORKDIR /app | |
| RUN pip install --no-cache-dir -U "transformers>=4.50" accelerate bitsandbytes peft huggingface_hub | |
| COPY aubin/ /app/aubin/ | |
| ENV AUBIN_MODEL=emrevrg/AUBIN-12B PORT=8009 | |
| EXPOSE 8009 | |
| CMD ["sh", "-c", "python -m aubin.cli serve --model $AUBIN_MODEL --port $PORT"] | |