add support for alpaca reflect training (#2)

scripts/finetune.py

@@ -37,9 +37,9 @@ from axolotl.prompt_tokenizers import (
     ShareGPTPromptTokenizingStrategy,
     LLAMA_DEFAULT_PAD_TOKEN,
     GPTeacherPromptTokenizingStrategy,
-    OpenAssistantPromptTokenizingStrategy,
+    OpenAssistantPromptTokenizingStrategy, AlpacaReflectionPTStrategy,
 )
-from axolotl.prompters import AlpacaPrompter, GPTeacherPrompter, ShareGPTPrompter
+from axolotl.prompters import AlpacaPrompter, GPTeacherPrompter, ShareGPTPrompter, ReflectAlpacaPrompter
 
 logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
 DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
@@ -395,6 +395,7 @@ def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
         )
         trainer_kwargs["optimizers"] = (adam_bnb_optim, lr_scheduler)
 
+    # TODO on_save callback to sync checkpoints to GCP/AWS in background
     if cfg.early_stopping_patience:
         early_stop_cb = EarlyStoppingCallback(
             cfg.early_stopping_patience,
@@ -540,6 +541,15 @@ def train(
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
                 datasets.append(ds_wrapper)
+            elif d.type == "reflection":
+                ds_strategy = AlpacaReflectionPTStrategy(
+                    ReflectAlpacaPrompter(),
+                    tokenizer,
+                    cfg.train_on_inputs,
+                    cfg.sequence_len,
+                )
+                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
+                datasets.append(ds_wrapper)
             elif d.type == "sharegpt":
                 ds_strategy = ShareGPTPromptTokenizingStrategy(
                     ShareGPTPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len