Pygmalion-6B Dev 4-bit Quantized
| Property | Value |
|---|---|
| Original Model | Pygmalion-6B |
| Quantization | 4-bit GPTQ |
| Group Size | 128 |
| Repository | HuggingFace |
What is pygmalion-6b_dev-4bit-128g?
This model is a highly optimized 4-bit quantized version of the Pygmalion-6B language model, created using GPTQ quantization techniques. The quantization process maintains the model's performance while significantly reducing its memory footprint and increasing inference speed.
Implementation Details
The model was quantized using a modified version of GPTQ-for-LLaMa, specifically adapted for the GPT-J architecture. The implementation uses 4-bit precision with a group size of 128, offering an optimal balance between model compression and performance.
- Quantization performed using custom GPTQ implementation
- 4-bit precision for maximum compression
- 128-size grouping for balanced performance
- Safetensors format for secure model loading
Core Capabilities
- Reduced memory footprint while maintaining model quality
- Faster inference times compared to full-precision model
- Compatible with standard GPT-J infrastructure
- Efficient deployment on resource-constrained systems
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization implementation, making it possible to run the 6B parameter model on devices with limited resources while maintaining good performance.
Q: What are the recommended use cases?
The model is ideal for deployment scenarios where memory efficiency is crucial, such as edge devices or systems with limited GPU memory. It's particularly suitable for applications requiring the capabilities of Pygmalion-6B but with stricter resource constraints.
