CardioLM: A multilingual suite of small language models for the cardiology domain
Paper • 2611.07940 • Published
AI & ML interests
Clinical language modeling. Medical NER+linking. Topic analysis. Text clustering. Auto summarisation. Diagnosis extraction.
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🚀 Can you turn an existing LLM into a diffusion language model in just a few hours on a single GPU?
We introduce "BYOD: Build Your Own DLM", a lightweight approach to converting autoregressive LLMs into diffusion models using LoRA, without modifying the original model weights.
✅ Works with Llama, Gemma, Qwen, and Ministral (and more!)
✅ Just 3–4 GPU hours for 7–9B models
✅ Open-source training code and Google Colab notebook
✅ Interactive demos to try all four models!
We're particularly interested in making diffusion language model research more accessible and reproducible.
Paper, code, and demos:
BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion (2610.41574)
Would love to hear your feedback and ideas for improving these conversions!
@AyoubBagheri @Ruurd @UMCU
We introduce "BYOD: Build Your Own DLM", a lightweight approach to converting autoregressive LLMs into diffusion models using LoRA, without modifying the original model weights.
✅ Works with Llama, Gemma, Qwen, and Ministral (and more!)
✅ Just 3–4 GPU hours for 7–9B models
✅ Open-source training code and Google Colab notebook
✅ Interactive demos to try all four models!
We're particularly interested in making diffusion language model research more accessible and reproducible.
Paper, code, and demos:
BYOD: Build Your Own DLM - Efficient Autoregressive-to-Diffusion Language Model Conversion (2610.41574)
Would love to hear your feedback and ideas for improving these conversions!
@AyoubBagheri @Ruurd @UMCU
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