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AutoGPTQ is an implementation of GPTQ (Quantized GPT) that optimizes large language models (LLMs) for faster inference by reducing their computational footprint while maintaining accuracy.
Features
- Efficient quantization for large language models
- Reduces memory usage without major performance loss
- Supports various precision levels (e.g., 4-bit, 8-bit)
- Compatible with Hugging Face Transformers
- Accelerates inference on GPUs and CPUs
- Helps deploy LLMs on resource-constrained hardware
Project Samples
License
MIT LicenseFollow AutoGPTQ
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