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Quantization-Aware Training in TorchAO (II)
In our previous Quantization-Aware Training (QAT) blog , we introduced the initial QAT flow in TorchAO for large language models targeting edge devices with ExecuTorch . Since then, we extended this...
PyTorch Blog··~3 min read
2-Minute Brief
According to PyTorch Blog: In our previous Quantization-Aware Training (QAT) blog , we introduced the initial QAT flow in TorchAO for large language models targeting edge devices with ExecuTorch . Since then, we extended this flow to also target fast CUDA kernels like the ones in MSLK for fast inference in vLLM , and incorporated this flow into popular fine-tuning frameworks like Unsloth and Axolotl . We also explored more advanced QAT techniques like PARQ for lower bit quantization (prototype): Unsloth integration : Reco
In our previous Quantization-Aware Training (QAT) blog , we introduced the initial QAT flow in TorchAO for large language models targeting edge devices with ExecuTorch . Since then, we extended this...
2-Minute Brief
According to PyTorch Blog: In our previous Quantization-Aware Training (QAT) blog , we introduced the initial QAT flow in TorchAO for large language models targeting edge devices with ExecuTorch . Since then, we extended this flow to also target fast CUDA kernels like the ones in MSLK for fast inference in vLLM , and incorporated this flow into popular fine-tuning frameworks like Unsloth and Axolotl . We also explored more advanced QAT techniques like PARQ for lower bit quantization (prototype): Unsloth integration : Reco