---
license: other
language:
- zh
- en
pipeline_tag: text-generation
tags:
- fortune-telling
- qwen
- qwen2.5
- qwen3
- gguf
- ollama
---

<div align="center">

# FortuneQwen3_4b

[**中文**](./README.md) | [**English**](./README_EN.md)

</div>

This is a 4B parameter model fine-tuned based on the Qwen3 architecture, specifically designed for Fortune Telling tasks. This repository provides merged Safetensors weights, GGUF quantized files, and a Modelfile for Ollama.

## About This Repository

This repository contains the model in three formats:

1.  **GGUF Quantized Model (Recommended)**:
    *   Filename: `FortuneQwen3_4b_q8_0.gguf` (or other versions)
    *   Description: Pre-converted GGUF format (Int8 quantized), ready for use with `llama.cpp` or `Ollama`.
2.  **Modelfile**:
    *   Filename: `Modelfile`
    *   Description: Configuration file for Ollama import, defining system prompts and parameters.
3.  **Hugging Face Safetensors**:
    *   Filename: `model.safetensors`, etc.
    *   Description: Full model parameters with LoRA weights already merged, suitable for Transformers-based inference, further fine-tuning, or exporting custom GGUF files.

## Quick Start

### Option 1: Using Ollama (Recommended)

You can quickly create an Ollama model using the pre-converted GGUF file found in this repository.

1.  **Clone this repository**:
    ```bash
    git clone https://huggingface.co/Tbata7/FortuneQwen3_4b
    cd FortuneQwen3_4b
    ```

2.  **Create the model**:
    ```bash
    # This uses the local Modelfile and GGUF file
    ollama create FortuneQwen3_q8:4b -f Modelfile
    ```

3.  **Run the model**:
    ```bash
    ollama run FortuneQwen3_q8:4b
    ```

### Option 2: Using llama.cpp

If you prefer to use the GGUF file directly with `llama.cpp`:

```bash
./llama-cli -m FortuneQwen3_4b_q8_0.gguf -p "Your question here..." -n 512
```

## Advanced Usage: Exporting Custom GGUF

If you wish to use a different quantization level (e.g., q4_k, q6_k, fp16), you can export a custom GGUF from the Safetensors weights using `llama.cpp`.

1.  **Prepare Environment**:
    Ensure you have `llama.cpp` python dependencies installed.

2.  **Convert Model**:
    Use the `convert_hf_to_gguf.py` script. You must specify the `--outtype` parameter to control the output type.

    *   **Export as FP16 (No quantization)**:
        ```bash
        python llama.cpp/convert_hf_to_gguf.py ./FortuneQwen3_4b --outfile FortuneQwen3_4b_fp16.gguf --outtype f16
        ```

    *   **Export as Int8 (q8_0)**:
        ```bash
        python llama.cpp/convert_hf_to_gguf.py ./FortuneQwen3_4b --outfile FortuneQwen3_4b_q8_0.gguf --outtype q8_0
        ```

    *   **Other Quantizations**:
        First export as f16, then use the `llama-quantize` tool:
        ```bash
        ./llama-quantize FortuneQwen3_4b_fp16.gguf FortuneQwen3_4b_q4_k_m.gguf q4_k_m
        ```

## Model Information

- **Base Architecture**: Qwen3:4B
- **Task**: Fortune Telling / I-Ching Interpretation
- **Context Window**: 32768
- **Fine-tuning Framework**: LLaMA-Factory

## Disclaimer

This model is for entertainment and research purposes only. Please believe in science.