Dolphin3.0-Llama3.2-3B-GGUF
| Property | Value |
|---|---|
| Base Model | Llama3.2-3B |
| Quantization Types | Multiple (F32 to IQ2) |
| Size Range | 1.23GB - 12.86GB |
| Model URL | https://huggingface.co/bartowski/Dolphin3.0-Llama3.2-3B-GGUF |
What is Dolphin3.0-Llama3.2-3B-GGUF?
Dolphin3.0-Llama3.2-3B-GGUF is a comprehensive collection of quantized versions of the Dolphin 3.0 language model, built on the Llama 3.2 3B architecture. This repository offers various quantization levels using llama.cpp's imatrix technology, providing options for different compute and memory constraints while maintaining model quality.
Implementation Details
The model implements advanced quantization techniques using llama.cpp release b4418, offering multiple compression formats from full F32 weights (12.86GB) down to highly compressed IQ2_M (1.23GB) variants. Each quantization level is carefully calibrated using imatrix options to optimize the quality-size trade-off.
- Multiple quantization options ranging from high-precision F32 to efficient IQ2_M
- Special variants with Q8_0 embeddings for improved quality in critical model components
- Support for online repacking for ARM and AVX CPU inference
- Optimized formats for different hardware configurations
Core Capabilities
- Flexible deployment options for various hardware configurations
- Maintained quality with significant size reduction (up to 90% compression)
- Special quantization formats for ARM and AVX architectures
- Support for both CPU and GPU inference
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 needs. The implementation of special quantization techniques for embeddings and output weights provides enhanced quality in critical model components.
Q: What are the recommended use cases?
For most general use cases, the Q4_K_M variant (2.02GB) is recommended as it provides a good balance of quality and size. For high-performance scenarios, Q6_K_L (2.74GB) is recommended, while for resource-constrained environments, IQ4_XS (1.83GB) offers good performance at a smaller size.
