Llama-3.2-3B-Instruct-uncensored-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.61B |
| Model Type | Transformer-based Language Model |
| Architecture | Llama-based Instruction Model |
| Author | mradermacher |
What is Llama-3.2-3B-Instruct-uncensored-GGUF?
This is a specialized variant of the Llama architecture, optimized for efficient deployment through GGUF quantization. The model offers multiple quantization options ranging from 1.6GB to 7.3GB in size, providing flexibility for different hardware configurations and performance requirements.
Implementation Details
The model is available in various quantization formats, with notable options including Q4_K_S and Q4_K_M which are recommended for their balance of speed and quality. The implementation includes both standard and IQ (Improved Quantization) variants, with file sizes ranging from 1.6GB (Q2_K) to 7.3GB (f16).
- Multiple quantization options (Q2_K through Q8_0)
- Improved Quantization (IQ) variants available
- Optimized for conversational tasks
- English language support
Core Capabilities
- Efficient inference with various quantization options
- Uncensored conversation generation
- Balanced performance across different hardware configurations
- Compatible with standard GGUF deployment tools
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its variety of quantization options and uncensored nature, making it suitable for deployments with different resource constraints while maintaining output flexibility.
Q: What are the recommended use cases?
The model is best suited for conversational applications where unrestricted outputs are desired. The Q4_K_S and Q4_K_M quantization variants are recommended for most use cases, offering a good balance of speed and quality.
