---
license: apache-2.0
language:
- en
library_name: gguf
pipeline_tag: text-generation
tags:
- mathematical-reasoning
- qwen3
- gguf
- quantized
- math
- reasoning
- fine-tuned
base_model: PinkPixel/Crystal-Think-V2
quantized_by: PinkPixel
---

<div align="center">
  <img src="crystal-think-v2-logo.png" alt="Crystal Think V2 Logo" width="400"/>
</div>

# 🧠 Crystal Think V2 - GGUF Quantized ✨

**Optimized GGUF Quantizations for Efficient Mathematical Reasoning**

> **🔗 Original Model:** [PinkPixel/Crystal-Think-V2](https://huggingface.co/PinkPixel/Crystal-Think-V2)  
> **📦 Quantized by:** Pink Pixel  
> **🏷️ License:** Apache 2.0  

---

## 📋 About This Repository

This repository contains **GGUF quantized versions** of Crystal Think V2, an advanced mathematical reasoning model based on Qwen3-4B. These quantized versions are optimized for **efficient inference** while maintaining excellent mathematical reasoning capabilities.

### 🎯 Original Model Features
- 🧮 **Advanced Mathematical Reasoning** with enhanced chain-of-thought
- 📐 **Multi-step Problem Solving** with clear explanations
- 💻 **Mathematical Code Generation** and algorithm explanation
- 🎯 **Enhanced `<think></think>` Reasoning Format**
- 📊 **85.2% GSM8K accuracy** (+8.8% over base Qwen3-4B)

---

## 📦 Available Quantizations

| Quantization | File Size | Use Case | Memory Required | Quality |
|-------------|-----------|----------|-----------------|---------|
| **Q4_K_M** | 2.3GB | Balanced efficiency | ~6GB RAM | Good |
| **Q5_K_M** | 2.7GB | Better quality | ~7GB RAM | Very Good |
| **Q6_K** | 3.1GB | High quality | ~8GB RAM | Excellent |
| **Q8_0** | 4.0GB | Maximum quality | ~10GB RAM | Near-Original |

### 💡 **Quantization Guide:**
- **Q4_K_M** - Best for limited hardware, good performance
- **Q5_K_M** - Recommended balance of speed and quality  
- **Q6_K** - High quality with reasonable speed
- **Q8_0** - Near-original quality, slower inference

---

## 🚀 Quick Start

### Using llama.cpp

```bash
# Download your preferred quantization
wget https://huggingface.co/PinkPixel/Crystal-Think-V2-GGUF/resolve/main/crystal-think-v2-q5_k_m.gguf

# Run with llama.cpp
./llama.cpp/main -m crystal-think-v2-q5_k_m.gguf -p "Solve this step by step: If x + 2y = 10 and 2x - y = 5, find x and y." -n 512
```

### Using llama-cpp-python

```python
from llama_cpp import Llama

# Load the model
llm = Llama(
    model_path="crystal-think-v2-q5_k_m.gguf",
    n_ctx=4096,  # Context length
    n_threads=8, # CPU threads
    verbose=False
)

# Mathematical reasoning example
prompt = """Solve this step by step:
A rectangle has a length that is 3 more than twice its width. If the perimeter is 42 cm, what are the dimensions?

Use <think></think> for your reasoning."""

response = llm(
    prompt,
    max_tokens=512,
    temperature=0.7,
    stop=["</SOLUTION>", "<|endoftext|>"]
)

print(response["choices"][0]["text"])
```

### Using Ollama

```bash
# Create Modelfile
echo 'FROM ./crystal-think-v2-q5_k_m.gguf' > Modelfile

# Create Ollama model
ollama create crystal-think-v2 -f Modelfile

# Run the model
ollama run crystal-think-v2 "What is the derivative of x^3 + 2x^2 - 5?"
```

---

## 🎯 Enhanced Reasoning Format

Crystal Think V2 uses a structured reasoning approach:

```
<think>
[Step-by-step reasoning process]
- Variable definitions
- Equation setup
- Mathematical operations
- Verification steps
</think>

<SOLUTION>
[Final organized answer]
1) Specific results
2) Numerical values
3) Units and context
</SOLUTION>
```

---

## 📊 Performance Benchmarks

### Original Model Performance
| Benchmark | Score | Improvement over Base |
|-----------|-------|----------------------|
| **GSM8K** | 85.2% | +8.8% |
| **MATH** | 42.1% | +10.4% |
| **Algebra** | 78.9% | +13.7% |
| **Geometry** | 71.3% | +12.5% |
| **Code Math** | 82.6% | +13.5% |

### GGUF Quantization Impact
- **Q8_0**: ~99% original performance
- **Q6_K**: ~97% original performance  
- **Q5_K_M**: ~95% original performance
- **Q4_K_M**: ~92% original performance

---

## 💻 Hardware Requirements

### Minimum Requirements
| Quantization | RAM | VRAM (GPU) | CPU |
|-------------|-----|-----------|-----|
| Q4_K_M | 6GB | 4GB | 4 cores |
| Q5_K_M | 7GB | 5GB | 4 cores |
| Q6_K | 8GB | 6GB | 6 cores |
| Q8_0 | 10GB | 8GB | 8 cores |

### Recommended for Best Performance
- **CPU**: Modern 8+ core processor
- **RAM**: 16GB+ system memory
- **GPU**: 8GB+ VRAM (optional, for GPU acceleration)

---

## 🔧 Installation & Dependencies

### llama.cpp
```bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
```

### llama-cpp-python
```bash
pip install llama-cpp-python
# For GPU support (optional)
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```

### Ollama
```bash
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
```

---

## 📚 Usage Examples

### Basic Mathematical Problem
```
Input: "What is the integral of 2x + 3?"
Expected: Step-by-step integration with explanation
```

### Complex Word Problem
```
Input: "A train travels 120 miles in 2 hours, then 180 miles in 3 hours. What's the average speed?"
Expected: Detailed solution with calculations
```

### Algebraic Reasoning
```
Input: "Solve the system: 3x + 2y = 12, x - y = 1"
Expected: Systematic solution using substitution or elimination
```

---

## 🔗 Related Links

- **🏠 Original Model:** [PinkPixel/Crystal-Think-V2](https://huggingface.co/PinkPixel/Crystal-Think-V2)
- **📖 Model Documentation:** [Crystal Think V2 README](https://huggingface.co/PinkPixel/Crystal-Think-V2/blob/main/README.md)
- **🛠️ llama.cpp:** [GitHub Repository](https://github.com/ggerganov/llama.cpp)
- **🐍 llama-cpp-python:** [PyPI Package](https://pypi.org/project/llama-cpp-python/)

---

## ⚠️ Limitations

- **Domain Focus**: Optimized for mathematical reasoning; may be less effective for general conversation
- **Quantization Trade-offs**: Lower quantizations may show reduced accuracy on complex problems
- **Language**: Primarily trained on English mathematical content
- **Hardware Dependency**: Performance varies significantly with hardware specifications

---

## 📈 Benchmarking Your Setup

Test your quantization choice with this sample problem:

```
Prompt: "A rectangular garden has a length that is 4 meters more than twice its width. The garden is surrounded by a walkway that is 2 meters wide on all sides. If the total area (garden + walkway) is 294 square meters, find the dimensions of the garden."

Expected: The model should show step-by-step reasoning and arrive at width ≈ 8.13m, length ≈ 20.26m
```

---

## 🤝 Contributing

Found an issue with the quantizations or have suggestions for improvements? Please open an issue or reach out!

---

## 📧 Contact & Support

- **Developer:** Pink Pixel
- **GitHub:** [https://github.com/pinkpixel-dev](https://github.com/pinkpixel-dev)
- **Website:** [https://pinkpixel.dev](https://pinkpixel.dev)
- **Email:** [admin@pinkpixel.dev](mailto:admin@pinkpixel.dev)

---

## 🙏 Acknowledgments

- **Original Model:** Crystal Think V2 by Pink Pixel
- **Base Model:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) by Qwen Team
- **Quantization Tools:** [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov
- **Training Dataset:** [NVIDIA OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning)

---

**Made with ❤️ by Pink Pixel** ✨  
*"Dream it, Pixel it"*