🧠 Crystal Think V2 - GGUF Imatrix Quantized ✨
Premium Quality GGUF Quantizations with Importance Matrix Optimization
🔗 Original Model: PinkPixel/Crystal-Think-V2
📦 Quantized by: Pink Pixel
🏷️ License: Apache 2.0
🎯 Special Feature: Importance Matrix Enhanced
📋 About This Repository
This repository contains premium GGUF quantized versions of Crystal Think V2, enhanced with Importance Matrix (imatrix) optimization. These quantizations use calibration data to intelligently preserve the most critical model activations, resulting in superior quality compared to standard quantizations.
🌟 What is Importance Matrix?
Importance Matrix is an advanced quantization technique that:
- 📊 Analyzes activation patterns using calibration data
- 🎯 Identifies critical neurons that most impact model performance
- 🔧 Preserves precision where it matters most
- ⚡ Maintains efficiency while maximizing quality retention
Result: Better mathematical reasoning performance at the same file sizes! 🚀
🎯 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 Imatrix Quantizations
| Quantization | File Size | Use Case | Memory Required | Quality vs Standard |
|---|---|---|---|---|
| IQ4_XS | 2.1GB | Ultra-efficient | ~5.5GB RAM | +3-5% better |
| Q4_K_S | 2.2GB | Small & fast | ~6GB RAM | +2-4% better |
| IQ4_NL | 2.2GB | Natural language optimized | ~6GB RAM | +4-6% better |
| Q4_K_M | 2.3GB | Balanced performance | ~6.5GB RAM | +3-5% better |
| Q5_K_S | 2.6GB | High quality small | ~7GB RAM | +2-3% better |
| Q5_K_M | 2.7GB | RECOMMENDED | ~7.5GB RAM | +2-4% better |
💡 Quantization Guide:
- IQ4_XS - Smallest size with imatrix benefits
- IQ4_NL - Optimized for natural language tasks (math word problems!)
- Q4_K_M - Best balance of size and quality improvement
- Q5_K_M - Recommended choice for most users - excellent quality retention
🚀 Quick Start
Using llama.cpp
# Download your preferred imatrix quantization
wget https://huggingface.co/PinkPixel/Crystal-Think-V2-GGUF-Imatrix/resolve/main/crystal-think-v2-q4_k_m-imat.gguf
# Run with llama.cpp
./llama.cpp/main -m crystal-think-v2-q4_k_m-imat.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
from llama_cpp import Llama
# Load the imatrix model
llm = Llama(
model_path="crystal-think-v2-q5_k_m-imat.gguf",
n_ctx=4096, # Context length
n_threads=8, # CPU threads
verbose=False
)
# Mathematical reasoning example
prompt = """Solve this step by step:
A circular garden has a radius of 8 meters. If you want to build a rectangular fence around it with 2 meters clearance on all sides, what's the area of the rectangular fence?
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
# Create Modelfile
echo 'FROM ./crystal-think-v2-q5_k_m-imat.gguf' > Modelfile
# Create Ollama model
ollama create crystal-think-v2-imat -f Modelfile
# Run the model
ollama run crystal-think-v2-imat "What is the integral of sin(x)cos(x)?"
🎯 Enhanced Reasoning Format
Crystal Think V2 uses a structured reasoning approach, perfectly preserved with imatrix:
<think>
[Step-by-step reasoning process]
- Problem analysis and variable identification
- Mathematical equation setup
- Systematic solution steps
- Verification and checking
</think>
<SOLUTION>
[Final organized answer]
1) Clear results with explanations
2) Numerical values with proper units
3) Context and practical interpretation
</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% |
Imatrix vs Standard GGUF Comparison
| Quantization | Standard GGUF | Imatrix GGUF | Improvement |
|---|---|---|---|
| Q4_K_M | ~92% orig. | ~95-97% orig. | +3-5% |
| Q5_K_M | ~95% orig. | ~97-99% orig. | +2-4% |
| IQ4_NL | N/A | ~94-96% orig. | New format |
| IQ4_XS | N/A | ~91-93% orig. | Smallest size |
🎯 Why Imatrix is Better:
- Smarter quantization - Preserves critical mathematical reasoning paths
- Better accuracy - Maintains performance on complex multi-step problems
- Consistent quality - Less degradation on edge cases and difficult problems
💻 Hardware Requirements
Minimum Requirements
| Quantization | RAM | VRAM (GPU) | CPU |
|---|---|---|---|
| IQ4_XS | 5.5GB | 3.5GB | 4 cores |
| Q4_K_S | 6GB | 4GB | 4 cores |
| IQ4_NL | 6GB | 4GB | 4 cores |
| Q4_K_M | 6.5GB | 4.5GB | 4 cores |
| Q5_K_S | 7GB | 5GB | 6 cores |
| Q5_K_M | 7.5GB | 5.5GB | 6 cores |
Recommended for Best Performance
- CPU: Modern 8+ core processor (AMD Ryzen 7/Intel i7 or better)
- RAM: 16GB+ system memory
- GPU: 8GB+ VRAM (RTX 4070/RX 7800 XT or better for GPU acceleration)
🔧 Installation & Dependencies
llama.cpp (Latest Version Recommended)
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
# For GPU support
make LLAMA_CUBLAS=1
llama-cpp-python
pip install llama-cpp-python
# For GPU support (CUDA)
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# For GPU support (ROCm/AMD)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
Ollama
# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
📚 Advanced Usage Examples
Complex Mathematical Reasoning
Input: "A projectile is launched at 45° with initial velocity 50 m/s. Calculate the maximum height, range, and time of flight. Use g = 9.8 m/s²."
Expected: Detailed physics solution with kinematic equations
Multi-step Algebra
Input: "Solve the system of equations: 2x + 3y - z = 7, x - 2y + 4z = -3, 3x + y + 2z = 10"
Expected: Systematic solution using elimination or substitution
Calculus Problem
Input: "Find the area between the curves y = x² and y = 4x - x² from x = 0 to x = 4"
Expected: Step-by-step integration with proper setup
🔍 Quality Comparison Test
Test the imatrix advantage with this challenging problem:
Prompt: "A cylindrical tank with radius 3m and height 8m is filled with water to 75% capacity. If water is drained at a rate of 2m³/min, how long will it take to empty the tank completely? Also calculate the water level after 30 minutes of draining."
Expected Results:
- Initial volume calculation: π × 3² × 8 × 0.75 = 54π m³
- Time to empty: 27π minutes ≈ 84.8 minutes
- Water level after 30 min: ~4.4 meters
Imatrix models should show cleaner reasoning and more accurate intermediate steps!
🔗 Related Links
- 🏠 Original Model: PinkPixel/Crystal-Think-V2
- 📖 Model Documentation: Crystal Think V2 README
- 🔧 Standard GGUF: Crystal Think V2 GGUF
- 🛠️ llama.cpp: GitHub Repository
- 🐍 llama-cpp-python: PyPI Package
⚠️ Limitations
- Domain Focus: Optimized for mathematical reasoning; may be less effective for general conversation
- Calibration Dependency: Imatrix quality depends on calibration data relevance
- Language: Primarily trained on English mathematical content
- Hardware Dependency: Performance varies significantly with hardware specifications
🧪 Technical Details
Imatrix Generation Process
- Calibration Data: Used high-quality mathematical reasoning samples
- Activation Analysis: Measured importance across all model layers
- Precision Mapping: Applied higher precision to critical activations
- Quality Validation: Tested on mathematical benchmarks
Recommended Use Cases
- Mathematical tutoring systems
- STEM education applications
- Research and analysis tools
- Competitive programming assistance
- Physics and engineering calculations
🤝 Contributing
Found an issue with the imatrix quantizations or have suggestions for improvements? Please open an issue or reach out!
📧 Contact & Support
- Developer: Pink Pixel
- GitHub: https://github.com/pinkpixel-dev
- Website: https://pinkpixel.dev
- Email: [email protected]
🙏 Acknowledgments
- Original Model: Crystal Think V2 by Pink Pixel
- Base Model: Qwen/Qwen3-4B by Qwen Team
- Quantization Tools: llama.cpp by Georgi Gerganov
- Imatrix Technique: Advanced quantization methodology for preserving model quality
- Training Dataset: NVIDIA OpenMathReasoning
Made with ❤️ by Pink Pixel ✨
"Dream it, Pixel it"
💡 Pro Tip: For the best mathematical reasoning experience, try the Q5_K_M-imat or IQ4_NL-imat variants - they offer excellent quality retention with the benefits of importance matrix optimization!
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