Instructions to use gab62-cam/RAMoEA-QA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gab62-cam/RAMoEA-QA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="gab62-cam/RAMoEA-QA")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("gab62-cam/RAMoEA-QA", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use gab62-cam/RAMoEA-QA with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gab62-cam/RAMoEA-QA" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gab62-cam/RAMoEA-QA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/gab62-cam/RAMoEA-QA
- SGLang
How to use gab62-cam/RAMoEA-QA with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "gab62-cam/RAMoEA-QA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gab62-cam/RAMoEA-QA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "gab62-cam/RAMoEA-QA" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gab62-cam/RAMoEA-QA", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use gab62-cam/RAMoEA-QA with Docker Model Runner:
docker model run hf.co/gab62-cam/RAMoEA-QA
RAMoEA-QA (Checkpoint)
RAMoEA-QA is a hierarchical generative model for Respiratory Audio Question Answering (RA-QA). It supports multiple question formats (open-ended, single-verify, multiple-choice) and both discrete labels (e.g., diagnosis/verification) and continuous targets (regression) within a single system.
Architecture (two-stage conditional specialization):
- Audio Mixture-of-Experts (Audio-MoE): routes each (audio, question) example to one pre-trained audio encoder expert.
- Language Mixture-of-Adapters (MoA): selects one LoRA adapter on a shared frozen LLM backbone (GPT-2) to match query intent and answer format.
Intended use
Research and decision-support experiments on respiratory-audio question-answering. Not a medical device.
Usage
This checkpoint is meant to be used with the accompanying codebase (audio encoder factory + routing + alignment):