SylvanL/ChatTCM-7B-SFT

Hugging Face
TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Oct 22, 2024License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Warm

SylvanL/ChatTCM-7B-SFT is a 7.6 billion parameter, 32K context length language model fine-tuned for Traditional Chinese Medicine (TCM) applications. Developed by SylvanL, it is based on the SylvanL/ChatTCM-7B-Pretrain model and underwent full supervised fine-tuning using the LlamaFactory framework and a specialized TCM dataset. This model excels at translating classical Chinese medical texts, providing clinical diagnostic logic and prescription recommendations aligned with mainstream TCM practitioners, and answering complex TCM knowledge questions, making it ideal for specialized medical NLP tasks.

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Overview

SylvanL/ChatTCM-7B-SFT is a 7.6 billion parameter language model with a 32K context length, specifically fine-tuned for Traditional Chinese Medicine (TCM). Built upon the SylvanL/ChatTCM-7B-Pretrain model, it underwent two epochs of full supervised fine-tuning using the LlamaFactory framework and the SylvanL/Traditional-Chinese-Medicine-Dataset-SFT. This specialization enables the model to handle complex TCM-related tasks without significant instruction loss or catastrophic forgetting.

Key Capabilities

  • Classical Text Translation: Translates ancient Chinese medical texts into modern Chinese, enhancing comprehension and application of TCM classics.
  • Clinical Diagnosis & Prescription: Provides clinical diagnostic logic and suggests herbal prescriptions, aligning with mainstream TCM practitioner approaches.
  • TCM Knowledge Q&A: Offers comprehensive and reliable answers to questions across various TCM knowledge domains.
  • Enhanced NLP for TCM Terminology: Improves foundational NLP capabilities for TCM terms, supporting applications like named entity recognition, relation extraction, association analysis, synonym disambiguation, and spell checking within the TCM context.

Good For

  • Researchers and practitioners needing to interpret classical Chinese medical literature.
  • Developing AI assistants for TCM clinical decision support.
  • Building intelligent systems for TCM knowledge management and education.
  • Applications requiring advanced natural language processing for specialized TCM terminology.

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