Xinghe1-9B-GGUF (杏核) contains the quantized GGUF versions of the Xinghe1-9B model, which is fine-tuned from the Qwen3.5-9B-Instruct architecture for the formalization, computational derivation, and clinical reasoning of Huangdi Neijing.
This repository provides multiple quantization formats compiled using llama-quantize for efficient local deployment and inference.
Xinghe1-9B-Q6_K.gguf (6.85 GB) — 6-bit balanced quantization, recommended for most setups.
Xinghe1-9B-Q4_K_M.gguf (5.24 GB) — 4-bit fast and lightweight quantization.
Note: All GGUF models in this repository have been fixed to exclude the Qwen3.5 MTP (Multi-Token Prediction) layers, avoiding metadata layer count conflicts. They can be directly loaded into LM Studio without errors.
Deployment & Usage
1. LM Studio
Search for zsyjsld/Xinghe1-9B-GGUF directly in the LM Studio search bar, select your preferred quantization version, and load the model.
2. Ollama
Run the 4-bit version using the following command:
ollama run hf.co/zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
Model Details
Developed by: zsyjsld
Base Model: Qwen/Qwen3.5-9B-Instruct
Fine-tuning Method: QLoRA SFT on V3 Double-Purity dataset.
Language(s): English & LaTeX (Internal reasoning formulas), English (Output explanations)
Xinghe1-9B is trained to strictly bind clinical concepts and perform dynamical derivations inside the <think> tag, and output natural, clean clinical TCM analyses outside the <think> tag.
The internal derivation is based on the unified meta-framework:
