Update README.md

README.md

@@ -85,14 +85,20 @@ run.influence.compute_self_influence(test_log) # Uncertainty estimation
 ### HuggingFace Integration
 Our software design allows for the seamless integration with HuggingFace's
 [Transformer](https://github.com/huggingface/transformers/tree/main), a popular DL framework
-that conveniently handles distributed training, data loading, etc. We plan to support more
-frameworks (e.g. Lightning) in the future.
+that conveniently handles distributed training, data loading, etc.
 
 ```python
 from transformers import Trainer, Seq2SeqTrainer
 from logix.huggingface import patch_trainer, LogIXArguments
 
-logix_args = LogIXArguments(project, config, lora=True, hessian="raw", save="grad")
+# Define LogIX arguments
+logix_args = LogIXArguments(project="myproject",
+                            config="config.yaml",
+                            lora=True,
+                            hessian="raw",
+                            save="grad")
+
+# Patch HF Trainer
 LogIXTrainer = patch_trainer(Trainer)
 
 # Pass LogIXArguments as TrainingArguments
@@ -108,6 +114,42 @@ trainer.influence()
 trainer.self_influence()
 ```
 
+### PyTorch Lightning Integration
+Similarly, we also support the LogIX + PyTorch Lightning integration. The code example
+is provided below.
+
+```python
+from lightning import LightningModule, Trainer
+from logix.lightning import patch, LogIXArguments
+
+class MyLitModule(LightningModule):
+    ...
+
+def data_id_extractor(batch):
+    return tokenizer.batch_decode(batch["input_ids"])
+
+# Define LogIX arguments
+logix_args = LogIXArguments(project="myproject",
+                            config="config.yaml",
+                            lora=True,
+                            hessian="raw",
+                            save="grad")
+
+# Patch Lightning Module and Trainer
+LogIXModule, LogIXTrainer = patch(MyLitModule,
+                                  Trainer,
+                                  logix_args=logix_args,
+                                  data_id_extractor=data_id_extractor)
+
+# Use patched Module and Trainer as before
+module = LogIXModule(user_args)
+trainer = LogIXTrainer(user_args)
+
+# Instead of trainer.fit(module, train_loader), use
+trainer.extract_log(module, train_loader)
+trainer.influence(module, train_loader)
+```
+
 Please check out [Examples](/examples) for more detailed examples!