Phi-3 conversation format, example training script and perplexity metric (#1582)

* phi-3 support and perplexity metric

* phi-3 chat template

* metrics updates

* chore: lint

* fix assertion on Tensor

* fix tests since tokenization happens in the metric

* fix perplexity value of shorter passage

---------

Co-authored-by: Wing Lian <wing.lian@gmail.com>

src/axolotl/utils/data/sft.py

@@ -474,12 +474,16 @@ def load_prepare_datasets(
             index=cfg.dataset_shard_idx,
         )
 
-    if split == "train" and cfg.val_set_size:
+    val_set_size = (
+        int(cfg.val_set_size) if cfg.val_set_size > 1 else float(cfg.val_set_size)
+    )
+
+    if split == "train" and val_set_size:
         # ensure we end up with the same fingerprint by doing rank0 first and being able to cache
         to_hash_train = (
             dataset._fingerprint  # pylint: disable=protected-access
             + "|"
-            + str(cfg.val_set_size)
+            + str(val_set_size)
             + "|"
             + "train"
             + "|"
@@ -488,7 +492,7 @@ def load_prepare_datasets(
         to_hash_test = (
             dataset._fingerprint  # pylint: disable=protected-access
             + "|"
-            + str(cfg.val_set_size)
+            + str(val_set_size)
             + "|"
             + "test"
             + "|"
@@ -498,9 +502,7 @@ def load_prepare_datasets(
         test_fingerprint = md5(to_hash_test)
 
         dataset = dataset.train_test_split(
-            test_size=int(cfg.val_set_size)
-            if cfg.val_set_size == int(cfg.val_set_size)
-            else cfg.val_set_size,
+            test_size=val_set_size,
             shuffle=False,
             seed=cfg.seed or 42,
             train_new_fingerprint=train_fingerprint,
@@ -535,6 +537,10 @@ def get_dataset_wrapper(
         "keep_in_memory": cfg.dataset_keep_in_memory is True,
     }
 
+    LOG.info(
+        f"Loading dataset with base_type: {d_base_type} and prompt_style: {d_prompt_style}"
+    )
+
     if (
         isinstance(dataset, Dataset)
         and "input_ids" in dataset.features