Create README.md

README.md

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+```
+model: single_linear
+config: Int4WeightOnlyConfig
+config version: 2
+torchao version: 0.13.dev
+```
+
+```
+import torch
+import io
+
+model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda"))
+
+from torchao.quantization import Int4WeightOnlyConfig, quantize_
+quant_config = Int4WeightOnlyConfig(group_size=128, version=1)
+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}-Int4WeightOnlyConfig-v2-0.13.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=True)
+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,
+)
+```