LLaMA-Mesh-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | LLaMA 3.1 |
| Base Model | Zhengyi/LLaMA-Mesh |
| Quantized By | bartowski |
What is LLaMA-Mesh-GGUF?
LLaMA-Mesh-GGUF is a comprehensive collection of quantized versions of the LLaMA-Mesh model, specifically optimized for mesh generation and text generation tasks. The model offers various quantization levels ranging from 2.95GB to 16.07GB, providing flexible options for different hardware configurations and performance requirements.
Implementation Details
The model utilizes llama.cpp for quantization and features multiple GGUF formats optimized with imatrix calibration. Each variant is carefully balanced between model size and performance, with specific optimizations for different hardware architectures including ARM, AVX2, and GPU acceleration.
- Multiple quantization options (Q2 to Q8) with different size-performance tradeoffs
- Special optimizations for ARM chips and AVX2/AVX512 CPUs
- Supports both K-quants and I-quants for different use cases
- Specialized versions with Q8_0 quantization for embedding and output weights
Core Capabilities
- Efficient mesh generation and text processing
- Optimized performance across different hardware configurations
- Flexible deployment options from 2.95GB to 16.07GB model sizes
- Support for both CPU and GPU acceleration
- Specialized prompt format for system and user interactions
Frequently Asked Questions
Q: What makes this model unique?
The model's unique strength lies in its variety of quantization options and optimization for mesh generation tasks, while maintaining high-quality text generation capabilities. The extensive range of quantized versions allows users to choose the perfect balance between model size and performance for their specific hardware setup.
Q: What are the recommended use cases?
For optimal performance, users should choose based on their hardware capabilities: Q6_K_L or Q6_K for maximum quality, Q5_K variants for balanced performance, and Q4_K_M for standard use cases. Users with limited RAM can opt for Q3_K or IQ3 variants, while those with ARM processors can benefit from specialized Q4_0_X_X versions.
