title: Med42-v2: A Suite of Clinical LLMs
summary: Med42-v2 introduces a suite of clinical large language models (LLMs) designed
  to address the limitations of generic models in healthcare settings. These
  models are built on Llama3 architecture and fine-tuned using specialized
  clinical data. They underwent multi-stage preference alignment to effectively
  respond to natural prompts. While generic models are often preference-aligned
  to avoid answering clinical queries as a precaution, Med42-v2 is specifically
  trained to overcome this limitation, enabling its use in clinical settings.
  Med42-v2 models demonstrate superior performance compared to the original
  Llama3 models in both 8B and 70B parameter configurations and GPT-4 across
  various medical benchmarks. These LLMs are developed to understand clinical
  queries, perform reasoning tasks, and provide valuable assistance in clinical
  environments. The models are now publicly available at
  https://huggingface.co/m42-health{https://huggingface.co/m42-health}.
paper_authors._id: 66bad2e2cd315359c8c9c20f
paper_authors.name: Clément Christophe
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:14:24.464Z
paper_authors._id: 628e39f4b1596566033b8d7b
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paper_authors.fullname: Clément Christophe
paper_authors.type: user
paper_authors.user: cchristophe
paper_authors._id: 66bad2e2cd315359c8c9c210
paper_authors.name: Praveen K Kanithi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-13T09:54:56.941Z
paper_authors._id: 65280984b794fe3d06544d77
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paper_authors.fullname: Praveenkumar
paper_authors.type: user
paper_authors.user: pkanithi
paper_authors._id: 66bad2e2cd315359c8c9c211
paper_authors.name: Tathagata Raha
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-13T09:14:36.456Z
paper_authors._id: 5f5f6c113c67af20d9945afb
paper_authors.avatarUrl: /avatars/06b2eb3a5d27864280d4d02e6d00d782.svg
paper_authors.fullname: Tathagata Raha
paper_authors.type: user
paper_authors.user: tathagataraha
paper_authors._id: 66bad2e2cd315359c8c9c212
paper_authors.name: Shadab Khan
paper_authors._id: 66bad2e2cd315359c8c9c213
paper_authors.name: Marco AF Pimentel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-24T07:53:56.497Z
paper_authors._id: 6454faafa13edf669cd74f36
paper_authors.avatarUrl: /avatars/355d16e28ca9cf5891368e43bcda6de5.svg
paper_authors.fullname: Marco Pimentel
paper_authors.type: user
paper_authors.user: mpimentel
paper_title: Med42-v2: A Suite of Clinical LLMs
paper_summary: Med42-v2 introduces a suite of clinical large language models (LLMs) designed
  to address the limitations of generic models in healthcare settings. These
  models are built on Llama3 architecture and fine-tuned using specialized
  clinical data. They underwent multi-stage preference alignment to effectively
  respond to natural prompts. While generic models are often preference-aligned
  to avoid answering clinical queries as a precaution, Med42-v2 is specifically
  trained to overcome this limitation, enabling its use in clinical settings.
  Med42-v2 models demonstrate superior performance compared to the original
  Llama3 models in both 8B and 70B parameter configurations and GPT-4 across
  various medical benchmarks. These LLMs are developed to understand clinical
  queries, perform reasoning tasks, and provide valuable assistance in clinical
  environments. The models are now publicly available at
  https://huggingface.co/m42-health{https://huggingface.co/m42-health}.
paper_ai_keywords: robust language models (LLMs) Llama3 architecture fine-tuned specialized clinical data Multi-stage preference training preference-aligned clinical queries reasoning tasks clinical environments