Add qa style data for alpaca instructions, fix one_cycle scheduler

src/axolotl/prompt_strategies/alpaca_chat.py

@@ -1,4 +1,4 @@
-from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
+from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy, InstructionPromptTokenizingStrategy
 from axolotl.prompters import AlpacaPrompter, PromptStyle
 
 
@@ -6,3 +6,18 @@ def load(tokenizer, cfg):
     return AlpacaPromptTokenizingStrategy(
         AlpacaPrompter(PromptStyle.chat), tokenizer, cfg.train_on_inputs, cfg.sequence_len
     )
+
+
+class AlpacaQAPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        return (
+            prompt["question"],
+            "",
+            prompt["answer"],
+        )
+
+
+def load_qa(tokenizer, cfg):
+    return AlpacaQAPromptTokenizingStrategy(
+        AlpacaPrompter(PromptStyle.chat), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )

src/axolotl/utils/trainer.py

@@ -23,7 +23,7 @@ class OneCycleLRSchedulerTrainer(Trainer):
         num_training_steps=num_training_steps
         pct_start = num_warmup_steps / num_training_steps
 
-        lr_scheduler = OneCycleLR(
+        self.lr_scheduler = OneCycleLR(
             optimizer,
             max_lr=self.args.learning_rate,
             total_steps=num_training_steps,
@@ -31,7 +31,7 @@ class OneCycleLRSchedulerTrainer(Trainer):
             div_factor=6,
         )
 
-        return lr_scheduler
+        return self.lr_scheduler
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):