Llama-3.1-DeepSeek-8B-DarkIdol-Instruct-1.2-Uncensored-GGUF
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Format | GGUF |
| Author | mradermacher |
| Source | Original Model |
What is Llama-3.1-DeepSeek-8B-DarkIdol-Instruct-1.2-Uncensored-GGUF?
This is a quantized version of the Llama 3.1 DeepSeek 8B model, specifically optimized for instruction-following tasks. The model has been converted to GGUF format and offers multiple quantization options to balance between model size and performance.
Implementation Details
The model provides various quantization options ranging from Q2_K (3.3GB) to Q8_0 (8.6GB), with F16 (16.2GB) available for maximum precision. Notable implementations include Q4_K_S and Q4_K_M which are recommended for their balance of speed and quality, while Q6_K offers very good quality and Q8_0 provides the best quality in compressed format.
- Multiple quantization options for different use cases
- Optimized GGUF format for efficient deployment
- Various size/quality tradeoffs available
- Both static and weighted/imatrix quantizations supported
Core Capabilities
- Instruction-following tasks
- Flexible deployment options with different quantization levels
- Optimized for performance vs size tradeoff
- Uncensored version for broader application scope
Frequently Asked Questions
Q: What makes this model unique?
This model offers a comprehensive range of quantization options in GGUF format, making it highly versatile for different deployment scenarios. The availability of both static and imatrix quantizations provides users with flexibility in choosing the right balance between model size and performance.
Q: What are the recommended use cases?
For most applications, the Q4_K_S (4.8GB) or Q4_K_M (5.0GB) variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while Q2_K and Q3_K variants are suitable for resource-constrained environments.
