Llama-3.1-DeepSeek-8B-DarkIdol-Instruct-1.2-Uncensored-i1-GGUF
| Property | Value |
|---|---|
| Base Model | Llama 3.1 DeepSeek |
| Parameters | 8B |
| Model Type | Instruction-tuned |
| Repository | Hugging Face |
What is Llama-3.1-DeepSeek-8B-DarkIdol-Instruct-1.2-Uncensored-i1-GGUF?
This model represents a sophisticated quantized version of the Llama 3.1 DeepSeek architecture, specifically designed for efficient deployment while maintaining performance. It offers multiple GGUF quantization options ranging from 2.1GB to 6.7GB, allowing users to balance between model size and quality based on their requirements.
Implementation Details
The model features various quantization types, including IQ (imatrix) and standard quantization options. The implementation provides multiple variants optimized for different use cases, from lightweight deployments to high-quality inference.
- Offers both IQ and standard quantization options (Q2_K to Q6_K)
- Size options ranging from 2.1GB (i1-IQ1_S) to 6.7GB (i1-Q6_K)
- Includes optimized versions for different performance/size trade-offs
- Features weighted/imatrix quantization for improved quality at smaller sizes
Core Capabilities
- Efficient deployment with various size options
- Optimized for instruction-following tasks
- Uncensored response generation
- Balanced performance across different quantization levels
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, particularly the IQ (imatrix) variants that often provide better quality than similar-sized standard quants. It's specifically designed for users who need flexibility in deployment while maintaining reasonable performance.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M (5.0GB) variant is recommended as it offers a good balance of speed and quality. For those with more limited resources, the IQ3 variants provide decent performance at smaller sizes. The Q6_K variant (6.7GB) offers the highest quality, comparable to static quantization.
