add alpaca multiple choice instruct dataset support

scripts/finetune.py

@@ -67,7 +67,7 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
         instruction = get_multi_line_input()
         if not instruction:
             return
-        prompt = prompter_module().build_prompt(instruction=instruction)
+        prompt: str = next(prompter_module().build_prompt(instruction=instruction))
         batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()

src/axolotl/prompt_tokenizers.py

@@ -92,6 +92,15 @@ class AlpacaPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
         )
 
 
+class AlpacaMultipleChoicePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        return (
+            prompt["question"],
+            "\n".join(f'- "{choice}"' for choice in prompt["choices"]),
+            prompt["explanation"],
+        )
+
+
 class JeopardyPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
     def parse_instruction_fields(self, prompt) -> (str, str, str):
         return (

src/axolotl/prompters.py

@@ -35,6 +35,10 @@ class JeopardyPrompter(AlpacaPrompter):
     prompt_input = "Below is a Jeopardy clue paired with input providing the category of the clue. Write a concise response that best answers tbe clue given the category.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
 
 
+class MultipleChoiceExplainPrompter(AlpacaPrompter):
+    prompt_input = "Choose the answer that best answers the question. Explain your reasoning.\n\n### Question:\n{instruction}\n\n### Choices:\n{input}\n\n### Response:\n"
+
+
 class CompletionPrompter(AlpacaPrompter):
     def build_prompt(self, instruction: str, input=None, output=None) -> Generator[str, None, None]:
         yield instruction

src/axolotl/utils/data.py

@@ -19,7 +19,7 @@ from axolotl.prompt_tokenizers import (
     AlpacaReflectionPTStrategy,
     ShareGPTPromptTokenizingStrategy,
     JeopardyPromptTokenizingStrategy,
-    CompletionPromptTokenizingStrategy,
+    CompletionPromptTokenizingStrategy, AlpacaMultipleChoicePromptTokenizingStrategy,
 )
 from axolotl.prompters import (
     AlpacaPrompter,
@@ -27,7 +27,7 @@ from axolotl.prompters import (
     ReflectAlpacaPrompter,
     ShareGPTPrompter,
     JeopardyPrompter,
-    CompletionPrompter,
+    CompletionPrompter, MultipleChoiceExplainPrompter,
 )
 
 
@@ -88,6 +88,12 @@ def load_tokenized_prepared_datasets(tokenizer, cfg, default_dataset_prepared_pa
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
                 datasets.append(ds_wrapper)
+            elif d.type == "explainchoice":
+                ds_strategy = AlpacaMultipleChoicePromptTokenizingStrategy(
+                    MultipleChoiceExplainPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+                )
+                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
+                datasets.append(ds_wrapper)
             elif d.type == "jeopardy":
                 ds_strategy = JeopardyPromptTokenizingStrategy(
                     JeopardyPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len