ozone-ai_0x-lite-GGUF
| Property | Value |
|---|---|
| Author | bartowski |
| Original Model | ozone-ai/0x-lite |
| Size Range | 5GB - 29.55GB |
| Format | GGUF |
What is ozone-ai_0x-lite-GGUF?
ozone-ai_0x-lite-GGUF is a comprehensive collection of quantized versions of the original 0x-lite model, optimized using llama.cpp's imatrix quantization technique. This collection provides various compression levels to accommodate different hardware capabilities and use cases, ranging from full F16 precision (29.55GB) to highly compressed IQ2_S format (5GB).
Implementation Details
The model utilizes a specific prompt format using im_start and im_end tokens for system, user, and assistant interactions. It offers multiple quantization types including Q8_0, Q6_K, Q5_K, Q4_K, and innovative IQ formats, each optimized for different performance-size trade-offs.
- Supports online repacking for ARM CPU inference
- Implements embed/output weight optimizations in specific variants
- Offers specialized quantizations for different hardware architectures
- Includes both K-quants and I-quants for various use cases
Core Capabilities
- Multiple quantization options for different RAM/VRAM configurations
- Optimized performance on various hardware architectures (CPU, GPU, Apple Silicon)
- Support for both high-quality and space-efficient implementations
- Compatible with LM Studio and various llama.cpp-based projects
Frequently Asked Questions
Q: What makes this model unique?
This model offers an extensive range of quantization options with clear trade-offs between quality and size, making it adaptable to various hardware configurations. The implementation of both K-quants and I-quants, along with specialized optimizations for different architectures, makes it highly versatile.
Q: What are the recommended use cases?
For maximum quality, use Q6_K_L (12.50GB) or Q5_K_M (10.51GB). For balanced performance, Q4_K_M (8.99GB) is recommended. For systems with limited RAM, the IQ3 and IQ2 variants offer surprisingly usable performance at smaller sizes. For Apple Silicon, Q4_1 offers improved tokens/watt efficiency.
