HuatuoGPT-o1-70B is a large language model (LLM) developed by FreedomIntelligence, specifically designed for complex medical reasoning. Before providing a final response, the model generates a detailed thought process that reflects and refines its reasoning. HuatuoGPT-o1-70B can handle intricate medical issues, providing thoughtful answers that are crucial for improving the quality and efficiency of medical decisions. The model is based on the LLaMA-3.1-70B architecture, supports English, and can be deployed on various tools like vllm or Sglang, or used for direct inference.

HuatuoGPT-o1-7B is a large language model (LLM) developed by FreedomIntelligence for the medical domain, specifically designed for advanced medical reasoning. The model generates complex reasoning processes before providing final answers, reflecting and refining its inference. HuatuoGPT-o1-7B supports both Chinese and English, handles complex medical queries, and outputs results in a 'thought-answer' format, which is crucial for improving the transparency and reliability of medical decisions. Based on Qwen2.5-7B, it has been specifically trained to meet the needs of the medical field.

The CHIEF (Clinical Histopathology Imaging Evaluation Foundation) model is a foundational model for cancer diagnosis and prognostic prediction in pathology. It extracts pathology imaging features through two complementary pre-training methods: unsupervised pre-training for tile-level feature identification and weakly supervised pre-training for pattern recognition in whole slides. The CHIEF model has been developed using 60,530 whole slide images (WSIs) covering 19 different anatomical sites, leveraging pre-training on a 44TB high-resolution pathology imaging dataset to extract informative micro-representations useful for cancer cell detection, tumor origin identification, molecular profiling, and prognostic prediction. The model has been validated on 19,491 whole slide images from 32 independent slide sets across 24 international hospitals and cohorts, achieving overall performance improvements of up to 36.1% over state-of-the-art deep learning methods, demonstrating its ability to address domain shift issues observed in varying population samples and slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology assessment of cancer patients.

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