Codestral-22B-v0.1-hf-AWQ
| Property | Value |
|---|---|
| Parameter Count | 3.33B |
| Model Type | Text Generation |
| Quantization | 4-bit AWQ |
| Downloads | 204,398 |
What is Codestral-22B-v0.1-hf-AWQ?
Codestral-22B-v0.1-hf-AWQ is a highly optimized, 4-bit quantized version of the original Codestral-22B model, created by bullerwins and quantized by Suparious. This model leverages Advanced Weight Quantization (AWQ) technology to significantly reduce the model size while maintaining performance quality.
Implementation Details
The model utilizes AWQ quantization, which offers faster Transformers-based inference compared to traditional GPTQ methods. It's designed for efficient deployment on NVIDIA GPUs, supporting both Linux and Windows platforms.
- Implements 4-bit precision for optimal storage efficiency
- Compatible with major frameworks including Text Generation Webui, vLLM, and Hugging Face TGI
- Requires specific packages: autoawq and autoawq-kernels
- Supports text generation inference with streaming capabilities
Core Capabilities
- Efficient text generation with reduced memory footprint
- Streaming text output support
- Integration with popular ML frameworks
- Optimized for production deployment
- Custom system message implementation
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its implementation of AWQ quantization, which provides superior inference speed compared to GPTQ while maintaining quality. Its high download count (200K+) demonstrates strong community adoption and reliability.
Q: What are the recommended use cases?
The model is ideal for production environments where efficient text generation is required while maintaining low memory usage. It's particularly suitable for applications requiring real-time text generation with limited computational resources.
