Nemo-12b-Humanize-KTO-Experimental-Latest-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Model Size | 12B parameters |
| Format | GGUF |
| Source | Based on Hugging Face model cgato/Nemo-12b-Humanize-KTO-Experimental-Latest |
What is Nemo-12b-Humanize-KTO-Experimental-Latest-GGUF?
This is a GGUF-formatted variant of the Nemo-12b model, specifically optimized for human-like responses. It offers multiple quantization options to balance between model size and performance, ranging from 4.9GB to 13.1GB.
Implementation Details
The model provides various quantization formats, each optimized for different use cases. The Q4_K_S and Q4_K_M variants are recommended for general use, offering a good balance of speed and quality. The Q8_0 variant provides the highest quality but requires more storage at 13.1GB.
- Multiple quantization options (Q2_K through Q8_0)
- Size ranges from 4.9GB to 13.1GB
- Includes specialized IQ4_XS quantization at 6.9GB
- Optimized for different performance/quality trade-offs
Core Capabilities
- Fast inference with recommended Q4_K variants
- High-quality text generation with Q6_K and Q8_0 variants
- Balanced performance with IQ-quants
- Flexible deployment options based on hardware constraints
Frequently Asked Questions
Q: What makes this model unique?
The model offers a comprehensive range of quantization options, allowing users to choose between size efficiency and quality. It's particularly notable for its humanized responses and the availability of both standard and IQ-based quantization formats.
Q: What are the recommended use cases?
For most applications, the Q4_K_S or Q4_K_M variants are recommended as they offer a good balance of speed and quality. For highest quality requirements, the Q8_0 variant is recommended, while resource-constrained environments might benefit from the lighter Q2_K or Q3_K variants.
