Text Generation
Transformers
English
multimodal
audio
healthcare
respiratory
question-answering
mixture-of-experts
lora
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
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README.md
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- **Audio Mixture-of-Experts (Audio-MoE):** routes each *(audio, question)* example to **one** pre-trained audio encoder expert.
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- **Language Mixture-of-Adapters (MoA):** selects **one** LoRA adapter on a shared **frozen** LLM backbone (GPT-2) to match query intent and answer format.
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> **Selected audio prefix** = aligned audio embeddings concatenated into the LLM input (soft prefix).
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## Intended use
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Research and decision-support experiments on
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## Usage
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This checkpoint is meant to be used with the accompanying codebase (audio encoder factory + routing + alignment):
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- **Audio Mixture-of-Experts (Audio-MoE):** routes each *(audio, question)* example to **one** pre-trained audio encoder expert.
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- **Language Mixture-of-Adapters (MoA):** selects **one** LoRA adapter on a shared **frozen** LLM backbone (GPT-2) to match query intent and answer format.
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## Intended use
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Research and decision-support experiments on respiratory-audio question-answering. **Not** a medical device.
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## Usage
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This checkpoint is meant to be used with the accompanying codebase (audio encoder factory + routing + alignment):
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