add support for defined train split

README.md

@@ -250,6 +250,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"article": "...", "question": "...", "answer": "..."}
   ```
+- `context_qa.load_v2`: in context question answering (alternate)
+  ```json
+  {"context": "...", "question": "...", "answer": "..."}
+  ```
 - `context_qa.load_404`: in context question answering from an article, with default response for no answer from context
   ```json
   {"article": "...", "unanswerable_question": "..."}
@@ -356,6 +360,12 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - path: data.jsonl # or json
       ds_type: json # see other options below
       type: alpaca
+
+  # dataset with splits, but no train split
+  dataset:
+    - path: knowrohit07/know_sql
+      type: context_qa.load_v2
+      train_on_split: validation
   ```
 
 - loading

src/axolotl/prompt_strategies/context_qa.py

@@ -24,6 +24,15 @@ def load(tokenizer, cfg):
     )
 
 
+def load_v2(tokenizer, cfg):
+    return ContextQaV2PromptTokenizingStrategy(
+        ContextV2Prompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+
+
 class AlpacaContextPrompter(AlpacaPrompter):
     """
     Customized system prompted for concise QA
@@ -50,6 +59,38 @@ def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
         )
 
 
+class ContextQaV2PromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    """
+    Tokenization Strategy to combine in-context article with a question and answer
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            "Context: "
+            + prompt["context"]
+            + "\nQuestion: "
+            + prompt["question"]
+            + "\n",
+            "",
+            "Answer: " + prompt["answer"],
+        )
+
+
+class ContextV2Prompter(AlpacaPrompter):
+    """
+    Customized system prompted for concise QA
+    """
+
+    system_prompt = ""
+    system_no_input_prompt = ""
+
+    def match_prompt_style(self):
+        # pylint: disable=duplicate-code
+        self.turn_format = "{instruction}\n{input}"
+        self.turn_no_input_format = "{instruction}"
+        self.system_format = "{system}"
+
+
 class AlpacaMissingInfoContextPromptTokenizingStrategy(
     InstructionPromptTokenizingStrategy
 ):

src/axolotl/utils/data.py

@@ -247,6 +247,16 @@ def for_d_in_datasets(dataset_configs):
                 d_prompt_style = d_type_split[1] if len(d_type_split) > 1 else None
             if "train" in ds:
                 ds = ds["train"]
+            elif (
+                isinstance(ds, DatasetDict)
+                and d.train_on_split
+                and d.train_on_split in ds
+            ):
+                ds = ds[d.train_on_split]
+            elif isinstance(ds, DatasetDict):
+                raise ValueError(
+                    f"no train split found for dataset {d.path}, you may specify a split with 'train_on_split: `"
+                )
             if (
                 "input_ids" in ds.features
                 and "attention_mask" in ds.features