teapotllm-GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Format | GGUF |
| Model Source | teapotai/teapotllm |
| Size Range | 0.2-0.6GB |
What is teapotllm-GGUF?
teapotllm-GGUF is a collection of quantized versions of the original teapotllm model, optimized for efficient deployment and reduced storage requirements. These quantizations offer various compression levels while maintaining different balances of performance and quality.
Implementation Details
The model provides multiple quantization options ranging from Q2 to Q8, plus a full F16 version. Each quantization type offers different trade-offs between model size, inference speed, and output quality. Notable implementations include Q4_K_S and Q4_K_M, which are recommended for their balance of speed and quality, and Q8_0, which provides the highest quality among the quantized versions.
- Multiple quantization options (Q2_K through Q8_0)
- File sizes ranging from 0.2GB to 0.6GB
- Optimized GGUF format for efficient deployment
- Various quality-size trade-offs for different use cases
Core Capabilities
- Fast inference with Q4_K variants (recommended)
- High-quality output with Q6_K and Q8_0 variants
- Compact deployment options starting at 0.2GB
- Support for different computational requirements
Frequently Asked Questions
Q: What makes this model unique?
This model provides a comprehensive range of quantization options for the teapotllm model, allowing users to choose the optimal balance between model size, speed, and quality for their specific use case.
Q: What are the recommended use cases?
For most applications, the Q4_K_S and 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 Q2 and Q3 variants are suitable for extremely resource-constrained environments.
