``` model: single_linear config: Int8DynamicActivationIntxWeightConfig config version: 2 torchao version: 0.14.dev ``` ``` import torch import io model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda")) from torchao.quantization import Int8DynamicActivationIntxWeightConfig, quantize_ from torchao.quantization.granularity import PerGroup version=2 quant_config = Int8DynamicActivationIntxWeightConfig( weight_dtype=torch.int4, weight_granularity=PerGroup(32), version=version ) quantize_(model, quant_config) example_inputs = (torch.randn(2, 32, dtype=torch.bfloat16, device="cuda"),) output = model(*example_inputs) # Push to hub USER_ID = "torchao-testing" MODEL_NAME = "single-linear" save_to = f"{USER_ID}/{MODEL_NAME}-Int8DynamicActivationIntxWeightConfig-v{version}-0.14.dev" from huggingface_hub import HfApi api = HfApi() buf = io.BytesIO() torch.save(model.state_dict(), buf) api.create_repo(save_to, repo_type="model", exist_ok=False) api.upload_file( path_or_fileobj=buf, path_in_repo="model.pt", repo_id=save_to, ) buf = io.BytesIO() torch.save(example_inputs, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_inputs.pt", repo_id=save_to, ) buf = io.BytesIO() torch.save(output, buf) api.upload_file( path_or_fileobj=buf, path_in_repo="model_output.pt", repo_id=save_to, ) ```