Added section on exporting weights from PyTorch using safetensors

README.md

@@ -240,7 +240,70 @@ This should print the top 5 imagenet categories for the image. The code for this
         println!("{:50} {:5.2}%", class, 100.0 * probability)
     }
 ```
+### Importing Pre-Trained Weights from PyTorch Using SafeTensors
 
+`safetensors` is a new simple format by HuggingFace for storing tensors. It does not rely on Python's `pickle` module, and therefore the tensors are not bound to the specific classes and the exact directory structure used when the model is saved. It is also zero-copy, which means that reading the file will require no more memory than the original file.
+
+For more information on `safetensors`, please check out https://github.com/huggingface/safetensors
+
+#### Installing `safetensors`
+
+You can install `safetensors` via the pip manager:
+
+```
+pip install safetensors
+```
+
+#### Exporting weights in PyTorch
+
+```python
+import torchvision
+from safetensors import torch as stt
+
+model = torchvision.models.resnet18(pretrained=True)
+stt.save_file(model.state_dict(), 'resnet18.safetensors')
+```
+
+*Note: the filename of the export must be named with  a `.safetensors` suffix for it to be properly decoded by `tch`.*
+
+#### Importing weights in `tch`
+
+```rust
+use anyhow::Result;
+use tch::{
+	Device,
+	Kind,
+	nn::VarStore,
+	vision::{
+		imagenet,
+		resnet::resnet18,
+	}
+};
+
+fn main() -> Result<()> {
+	// Create the model and load the pre-trained weights
+	let mut vs = VarStore::new(Device::cuda_if_available());
+	let model = resnet18(&vs.root(), 1000);
+	vs.load("resnet18.safetensors")?;
+
+	// Load the image file and resize it to the usual imagenet dimension of 224x224.
+	let image = imagenet::load_image_and_resize224("dog.jpg")?
+		.to_device(vs.device());
+
+	// Apply the forward pass of the model to get the logits
+	let output = image
+		.unsqueeze(0)
+		.apply_t(&model, false)
+		.softmax(-1, Kind::Float);
+
+	// Print the top 5 categories for this image.
+    for (probability, class) in imagenet::top(&output, 5).iter() {
+        println!("{:50} {:5.2}%", class, 100.0 * probability)
+    }
+
+    Ok(())
+}
+```
 
 Further examples include:
 * A simplified version of