# Large model compression

An efficient language model based on Qwen3-32B with FP8 dynamic quantization, significantly reducing memory requirements and improving computational efficiency

A model fine-tuned based on Meta Llama-3.1-70B-Instruct, optimized with NVIDIA HelpSteer2 dataset, supporting text generation tasks.

FP8 quantized version of QwQ-32B, reducing storage and memory requirements by 50% through dynamic quantization while maintaining 99.75% of the original model accuracy

The 4-bit quantized version of Molmo-7B-O, significantly reducing the memory requirement and suitable for environments with limited resources.