add tokenizer_save_jinja_files to keep legacy behavior of including chat template in tokenizer_config.json (#3093)

* add tokenizer_save_jinja_files to keep legacy behavior of including chat template in tokenizer_config.json

* fix test import

tests/e2e/test_tokenizer.py

@@ -0,0 +1,63 @@
+"""
+e2e test for saving the tokenizer
+"""
+
+from unittest.mock import patch
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_model_output_exists
+
+
+def test_tokenizer_no_save_jinja_files(temp_dir):
+    # pylint: disable=duplicate-code
+    cfg = DictDefault(
+        {
+            "base_model": "HuggingFaceTB/SmolLM2-135M",
+            "tokenizer_type": "AutoTokenizer",
+            "sequence_len": 1024,
+            "load_in_8bit": True,
+            "adapter": "lora",
+            "lora_r": 8,
+            "lora_alpha": 16,
+            "lora_dropout": 0.05,
+            "lora_target_linear": True,
+            "val_set_size": 0.02,
+            "special_tokens": {
+                "pad_token": "<|endoftext|>",
+            },
+            "chat_template": "chatml",
+            "datasets": [
+                {
+                    "path": "mhenrichsen/alpaca_2k_test",
+                    "type": "alpaca",
+                },
+            ],
+            "num_epochs": 1,
+            "micro_batch_size": 2,
+            "gradient_accumulation_steps": 1,
+            "output_dir": temp_dir,
+            "learning_rate": 0.00001,
+            "optimizer": "adamw_torch_fused",
+            "lr_scheduler": "cosine",
+            "max_steps": 5,
+            "save_first_step": False,
+            "fp16": False,
+            "tokenizer_save_jinja_files": False,
+        }
+    )
+
+    cfg = validate_config(cfg)
+    normalize_config(cfg)
+    dataset_meta = load_datasets(cfg=cfg)
+
+    with patch("axolotl.train.execute_training"):
+        train(cfg=cfg, dataset_meta=dataset_meta)
+
+    check_model_output_exists(temp_dir, cfg)
+    with open(f"{temp_dir}/tokenizer_config.json", "r", encoding="utf-8") as f:
+        tokenizer_config = f.read()
+        assert "chat_template" in tokenizer_config