Completion only fine-tuning of instruction models with collections of HF datasets (#1103)

- Optional completion only fine-tuning with `--mask-prompt`
- Collections of Hugging Face datasets

---------

Co-authored-by: Awni Hannun <awni@apple.com>

llms/tests/test_datsets.py

@@ -78,14 +78,15 @@ class TestDatasets(unittest.TestCase):
         self.assertTrue(isinstance(train, datasets.ChatDataset))
 
     def test_hf(self):
+        hf_args = {
+            "name": "billsum",
+            "prompt_feature": "text",
+            "completion_feature": "summary",
+            "train_split": "train[:2%]",
+            "valid_split": "train[-2%:]",
+        }
         args = types.SimpleNamespace(
-            hf_dataset={
-                "name": "billsum",
-                "prompt_feature": "text",
-                "completion_feature": "summary",
-                "train_split": "train[:2%]",
-                "valid_split": "train[-2%:]",
-            },
+            hf_dataset=hf_args,
             test=False,
             train=True,
         )
@@ -97,6 +98,16 @@ class TestDatasets(unittest.TestCase):
         self.assertTrue(len(valid[0]) > 0)
         self.assertEqual(len(test), 0)
 
+        args = types.SimpleNamespace(
+            hf_dataset=[hf_args, hf_args],
+            test=False,
+            train=True,
+        )
+        train_double, valid_double, test_double = datasets.load_dataset(args, tokenizer)
+        self.assertEqual(2 * len(train), len(train_double))
+        self.assertEqual(2 * len(valid), len(valid_double))
+        self.assertEqual(2 * len(test), len(test_double))
+
 
 if __name__ == "__main__":
     unittest.main()