gpt4-x-alpaca-13b-native-4bit-128g
| Property | Value |
|---|---|
| Model Size | 13B parameters |
| Quantization | 4-bit |
| Framework | PyTorch |
| Group Size | 128 |
What is gpt4-x-alpaca-13b-native-4bit-128g?
This model is a highly optimized 4-bit quantized version of the GPT4-X-Alpaca language model, specifically designed for efficient deployment using CUDA. Based on the LLaMA architecture and trained on the GPTeacher dataset, it offers an excellent balance between performance and resource utilization.
Implementation Details
The model was quantized using GPTQ-for-LLaMa, implementing both CUDA and Triton versions. The CUDA version was generated using the command CUDA_VISIBLE_DEVICES=0 with 4-bit quantization, true-sequential processing, and a group size of 128.
- GPTQ 4-bit quantization optimization
- True-sequential processing implementation
- 128 group size for optimal performance
- CUDA-optimized architecture
Core Capabilities
- Efficient text generation and processing
- Reduced memory footprint while maintaining performance
- Compatible with text-generation-inference systems
- Optimized for GPU deployment
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the capabilities of the original GPT4-X-Alpaca model. Its CUDA optimization makes it particularly suitable for production deployments.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient text generation and processing, particularly in resource-constrained environments where maintaining model quality while reducing memory usage is crucial.
