Godners/dao-zang-sft-qwen2.5-0.5b

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 9, 2026License:mitArchitecture:Transformer Open Weights Featherless Exclusive Cold

Godners/dao-zang-sft-qwen2.5-0.5b is a 0.5 billion parameter Qwen2.5-Instruct based causal language model, fine-tuned by Godners specifically for question answering within the Daozang (Chinese classical Taoist texts). This model excels as a specialized expert in Daozang-related inquiries, providing concept explanations, background information, and citation understanding, and is not intended as a general-purpose LLM. It was trained using 100,000 synthetic QA pairs derived from the Daozang corpus, ensuring answers are referenced from the original texts without fabrication.

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Daozang Q&A Domain Model

This model, dao-zang-sft-qwen2.5-0.5b, is a specialized 0.5 billion parameter language model developed by Godners. It is built upon the Qwen/Qwen2.5-0.5B-Instruct base model and has been instruction fine-tuned (SFT) using LoRA with 100,000 synthetic question-answer pairs from the "Daozang" (Chinese classical Taoist texts).

Key Capabilities

  • Daozang Question Answering: Functions as an expert for inquiries related to the Daozang, including:
    • Concept explanations for terms, names, and objects.
    • Background information on chapters and texts.
    • Understanding and interpreting citations.
  • Non-Generative Answers: Answers are designed to reference original texts and avoid fabrication, based on rule-templated and LLM-synthesized training data.
  • Deployment Flexibility: Available as a complete HuggingFace safetensors model and in GGUF(f16) format for deployment with tools like Ollama and llama.cpp.

Intended Use Cases

  • Academic Research: Ideal for studies involving traditional Chinese culture and Taoist scriptures.
  • RAG Systems: Serves as a strong starting point for downstream conversational fine-tuning in Retrieval-Augmented Generation (RAG) applications focused on the Daozang.

Known Limitations

  • Specialized Scope: Optimized exclusively for Daozang-related questions; general conversational quality is lower than the base model.
  • Synthetic Data: Individual entries may have minor linguistic flaws or imprecise interpretations due to the synthetic nature of the training data.
  • Capacity: As a 0.5B model, it has limited complex reasoning and long-text comprehension abilities. It may hallucinate when encountering unseen text combinations, requiring users to verify critical assertions against original sources.
  • Ethical Considerations: Outputs related to health, alchemy, talismans, or medicine should not be considered medical or practice advice. Adherence to licensing terms of the base model and dataset is required.

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