Update to adapt to sharegpt datasets with "assistant" rather than "gp…

src/axolotl/prompt_strategies/sharegpt.py

@@ -24,7 +24,7 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     )
     field_human = ds_cfg["field_human"] if ds_cfg and "field_human" in ds_cfg else None
     field_model = ds_cfg["field_model"] if ds_cfg and "field_model" in ds_cfg else None
-    return SimpleShareGPTPromptTokenizingStrategy(
+    strategy = SimpleShareGPTPromptTokenizingStrategy(
         ShareGPTPrompterV2(
             conversation=conversation,
             role_key_model=field_model,
@@ -34,6 +34,9 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
+    if ds_cfg and "strict" in ds_cfg:
+        strategy.strict = ds_cfg["strict"]
+    return strategy
 
 
 def load_role(tokenizer, cfg):
@@ -59,8 +62,26 @@ class SimpleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):
     basic sharegpt strategy to grab conversations from the sample row
     """
 
+    _strict = True
+
+    @property
+    def strict(self):
+        return self._strict
+
+    @strict.setter
+    def strict(self, strict):
+        self._strict = strict
+
     def get_conversation_thread(self, prompt):
-        return prompt["conversations"]
+        conversations = prompt["conversations"]
+        if self.strict:
+            return conversations
+        # remap roles - allow for assistant turn
+        role_map = {"human": "human", "assistant": "gpt", "gpt": "gpt"}
+        turns = [
+            {"from": role_map[t["from"]], "value": t["value"]} for t in conversations
+        ]
+        return turns
 
 
 class SimpleRoleShareGPTPromptTokenizingStrategy(ShareGPTPromptTokenizingStrategy):