diff --git a/README.md b/README.md
index c8b670f..0a64ae9 100644
--- a/README.md
+++ b/README.md
@@ -156,3 +156,19 @@ For info about the arguments to the script, do:
 ```bash
 python3 scripts/subset_data.py --help
 ```
+
+# Exporting checkpoints to huggingface compatible model
+You can convert the checkpoints saved by the training script to a model that can be run with any huggingface-compatible inference engine (e.g. transformers, vLLM) using our export script.
+The export script takes the training config as a positional argument and 2 keyword arguments, `ckpt.resume` which is the path to the checkpoint, `ckpt.path` which is the path you wish to save the converted model.
+You may also pass the `torch_dtype` argument to either `float32` or `bfloat16` to specify the precision of the exported model weights. The default `torch_dtype` is `float32`.
+
+Example export command:
+```bash
+python scripts/export_dcp.py @configs/10B/H100.toml --ckpt.path /path/to/save/converted_model --ckpt.resume /path/to/ckpt/step_84000 --torch_dtype bfloat16
+```
+
+You can then upload the model to huggingface using huggingface-cli:
+```bash
+# Usage:  huggingface-cli upload [repo_id] [local_path] [path_in_repo]
+huggingface-cli upload mymodel /path/to/save/converted_model . --private
+```
diff --git a/scripts/export_dcp.py b/scripts/export_dcp.py
index 7d1bfc8..51538ba 100644
--- a/scripts/export_dcp.py
+++ b/scripts/export_dcp.py
@@ -24,7 +24,7 @@
 
 class ExportConfig(Config):
     save_format: Literal["pt", "safetensors"] = "safetensors"
-    torch_dtype: Literal["float32", "bfloat16"] = "bfloat16"
+    torch_dtype: Literal["float32", "bfloat16"] = "float32"
     with_debug_automap: bool = False