Meta-Llama-3.1-8B-Instruct-abliterated-GGUF
| Property | Value |
|---|---|
| Parameter Count | 8.03B |
| License | Llama3.1 |
| Base Model | Meta-Llama-3.1-8B-Instruct |
| Quantization Types | Multiple (F32 to IQ2_M) |
What is Meta-Llama-3.1-8B-Instruct-abliterated-GGUF?
This is a comprehensive quantization suite of the Meta-Llama-3.1-8B-Instruct model, optimized using llama.cpp's latest techniques. The model offers various quantization options ranging from full 32GB F32 weights down to highly compressed 2.95GB versions, making it adaptable to different hardware configurations while maintaining performance.
Implementation Details
The model uses imatrix quantization with a specialized dataset, offering multiple compression levels optimized for different use cases. Each variant is carefully balanced between model size and performance, with specific recommendations for different hardware setups.
- Multiple quantization options (F32, Q8_0, Q6_K, Q5_K, Q4_K, Q3_K, IQ4, IQ3, IQ2)
- Specialized prompt format for optimal interaction
- Compatible with LM Studio and various inference engines
- GGUF format for efficient deployment
Core Capabilities
- Text generation and conversational AI
- Flexible deployment options for different hardware configurations
- Optimized performance with various quantization levels
- Support for both CPU and GPU acceleration
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options using state-of-the-art techniques, allowing users to choose the perfect balance between model size and performance for their specific hardware setup. The imatrix quantization method provides superior performance compared to traditional quantization approaches.
Q: What are the recommended use cases?
For maximum performance, choose a quantization level 1-2GB smaller than your GPU's VRAM. For optimal quality, select a version that fits within your combined system RAM and GPU VRAM. K-quants (Q5_K_M, Q4_K_M) are recommended for general use, while I-quants are better for newer hardware with cuBLAS or rocBLAS support.
