Create README.md

README.md

@@ -0,0 +1,45 @@
+```
+import torch
+import io
+
+model = torch.nn.Sequential(torch.nn.Linear(32, 256, dtype=torch.bfloat16, device="cuda"))
+
+from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig, PerRow
+quant_config = Float8DynamicActivationFloat8WeightConfig(granularity=PerRow(), 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}-Float8DynamicActivationFloat8WeightConfig-v1-0.13-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.bin",
+    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,
+)
+```