Llama-3.1-WhiteRabbitNeo-2-8B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | LLaMA 3.1 |
| Base Model | WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-8B |
| Quantization | Multiple GGUF formats |
What is Llama-3.1-WhiteRabbitNeo-2-8B-GGUF?
This is a comprehensive collection of quantized versions of the Llama-3.1-WhiteRabbitNeo model, specifically optimized for different hardware configurations and memory constraints. The model uses imatrix quantization techniques to provide various compression levels while maintaining performance.
Implementation Details
The model comes in multiple quantization formats ranging from full F16 (16.07GB) down to highly compressed IQ2_M (2.95GB) versions. Each variant is optimized using llama.cpp with imatrix calibration, offering different trade-offs between model size and performance.
- Supports multiple quantization formats (Q8_0, Q6_K, Q4_K, Q3_K, IQ4, IQ3, IQ2)
- Specialized versions for ARM inference with SVE and i8mm support
- Optimized embed/output weights in certain variants
- Compatible with LM Studio and various inference engines
Core Capabilities
- Text generation and conversation abilities
- Memory-efficient deployment options
- Hardware-specific optimizations
- Flexible prompt format support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. The use of imatrix quantization and specialized optimizations for different architectures makes it highly versatile.
Q: What are the recommended use cases?
For users with high-end hardware, the Q6_K_L or Q5_K_M variants are recommended for optimal quality. For systems with limited RAM, the Q4_K_M offers a good balance, while IQ3_XS and IQ2_M provide surprisingly usable performance on very constrained systems.
