Instructions to use zsyjsld/Xinghe1-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zsyjsld/Xinghe1-9B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("zsyjsld/Xinghe1-9B-GGUF", dtype="auto")

llama-cpp-python

How to use zsyjsld/Xinghe1-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="zsyjsld/Xinghe1-9B-GGUF",
	filename="Xinghe1-9B-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use zsyjsld/Xinghe1-9B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use zsyjsld/Xinghe1-9B-GGUF with Ollama:
```
ollama run hf.co/zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
```

Unsloth Studio

How to use zsyjsld/Xinghe1-9B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zsyjsld/Xinghe1-9B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for zsyjsld/Xinghe1-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for zsyjsld/Xinghe1-9B-GGUF to start chatting

How to use zsyjsld/Xinghe1-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "zsyjsld/Xinghe1-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use zsyjsld/Xinghe1-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use zsyjsld/Xinghe1-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "zsyjsld/Xinghe1-9B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use zsyjsld/Xinghe1-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/zsyjsld/Xinghe1-9B-GGUF:Q4_K_M
```

Lemonade

How to use zsyjsld/Xinghe1-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull zsyjsld/Xinghe1-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Xinghe1-9B-GGUF-Q4_K_M

List all available models

lemonade list

Xinghe1-9B-GGUF (杏核1代-9B GGUF)

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.

Released Quantizations

Xinghe1-9B-BF16.gguf (16.69 GB) — Native best-quality Bfloat16 precision, recommended for GPU setups.
Xinghe1-9B-Q8_0.gguf (8.87 GB) — 8-bit high-quality quantization, minimal quality loss.
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)
License: Apache 2.0 (for code/dataset) & Qwen License Agreement (for model weights)

Technical Architecture

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:

$\dot{x} = F(x) + G(x, \mu(t))$

$y = h(x) + \epsilon$

Downloads last month: 173

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

6-bit

8-bit

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