Update README.md

README.md

@@ -66,27 +66,33 @@ To get started, simply install `datasets` with `pip install datasets` and load t
 
 ```python
 from datasets import load_dataset
+from huggingface_hub import snapshot_download
 
-# Load the training, validation and test (IID) splits
-train = load_dataset("McGill-NLP/weblinx", split="train")
 valid = load_dataset("McGill-NLP/weblinx", split="validation")
-test = load_dataset("McGill-NLP/weblinx", split="test")
-
-# Load one of the 4 out-of-domain splits (test_web, test_vis, test_geo, test_cat)
-test_web = load_dataset("McGill-NLP/weblinx", split="test_web")
 
-# Download and read template
 snapshot_download(
-  repo_id="McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="template.txt", local_dir="./"
+    "McGill-NLP/WebLINX", repo_type="dataset", allow_patterns="templates/llama.txt", local_dir="./"
 )
-with open('template.txt') as f:
+with open('llama.txt') as f:
     template = f.read()
 
-# Get the input text for a model by formatting the template with the correct
-turn_data = valid[0]
-input_text = template.format(**turn_data)
+turn = valid[0]
+turn_text = template.format(**turn)
+```
+
+You can now use `turn_text` as an input to LLaMA-style models. For example, you can use Sheared-LLaMA:
+```python
+
+from transformers import pipeline
+
+action_model = pipeline(
+    model="McGill-NLP/Sheared-LLaMA-2.7B-weblinx", device=0, torch_dtype='auto'
+)
+out = action_model(turn_text, return_full_text=False, max_new_tokens=64, truncation=True)
+pred = out[0]['generated_text']
 
-# input_text can be given to a model
+print("Ref:", turn["action"])
+print("Pred:", pred)
 ```
 
 ## Raw Data