PinkPixel
/

Crystal-Think-V2-Imatrix-GGUF

@@ -1,336 +1,336 @@
----
-license: apache-2.0
-language:
-- en
-library_name: gguf
-pipeline_tag: text-generation
-tags:
-- mathematical-reasoning
-- qwen3
-- gguf
-- quantized
-- imatrix
-- importance-matrix
-- 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="300"/>
-</div>
-# 🧠 Crystal Think V2 - GGUF Imatrix Quantized ✨
-**Premium Quality GGUF Quantizations with Importance Matrix Optimization**
-> **🔗 Original Model:** [PinkPixel/Crystal-Think-V2](https://huggingface.co/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
-```bash
-# 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
-```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
-```bash
-# 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)
-```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp
-make
-# For GPU support
-make LLAMA_CUBLAS=1
-```
-### llama-cpp-python
-```bash
-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
-```bash
-# 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](https://huggingface.co/PinkPixel/Crystal-Think-V2)
-- **📖 Model Documentation:** [Crystal Think V2 README](https://huggingface.co/PinkPixel/Crystal-Think-V2/blob/main/README.md)
-- **🔧 Standard GGUF:** [Crystal Think V2 GGUF](https://huggingface.co/PinkPixel/Crystal-Think-V2-GGUF)
-- **🛠️ 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
-- **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
-1. **Calibration Data**: Used high-quality mathematical reasoning samples
-2. **Activation Analysis**: Measured importance across all model layers
-3. **Precision Mapping**: Applied higher precision to critical activations
-4. **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](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
-- **Imatrix Technique:** Advanced quantization methodology for preserving model quality
-- **Training Dataset:** [NVIDIA OpenMathReasoning](https://huggingface.co/datasets/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!

+---
+license: apache-2.0
+language:
+- en
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+- mathematical-reasoning
+- qwen3
+- gguf
+- quantized
+- imatrix
+- importance-matrix
+- 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 Imatrix Quantized ✨
+**Premium Quality GGUF Quantizations with Importance Matrix Optimization**
+> **🔗 Original Model:** [PinkPixel/Crystal-Think-V2](https://huggingface.co/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
+```bash
+# 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
+```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
+```bash
+# 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)
+```bash
+git clone https://github.com/ggerganov/llama.cpp
+cd llama.cpp
+make
+# For GPU support
+make LLAMA_CUBLAS=1
+```
+### llama-cpp-python
+```bash
+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
+```bash
+# 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](https://huggingface.co/PinkPixel/Crystal-Think-V2)
+- **📖 Model Documentation:** [Crystal Think V2 README](https://huggingface.co/PinkPixel/Crystal-Think-V2/blob/main/README.md)
+- **🔧 Standard GGUF:** [Crystal Think V2 GGUF](https://huggingface.co/PinkPixel/Crystal-Think-V2-GGUF)
+- **🛠️ 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
+- **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
+1. **Calibration Data**: Used high-quality mathematical reasoning samples
+2. **Activation Analysis**: Measured importance across all model layers
+3. **Precision Mapping**: Applied higher precision to critical activations
+4. **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](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
+- **Imatrix Technique:** Advanced quantization methodology for preserving model quality
+- **Training Dataset:** [NVIDIA OpenMathReasoning](https://huggingface.co/datasets/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!