diff --git a/README.md b/README.md
index 27aec72db..4929987cb 100644
--- a/README.md
+++ b/README.md
@@ -195,6 +195,10 @@ Have dataset(s) in one of the following format (JSONL recommended):
   ```json
   {"message_1": "...", "message_2": "..."}
   ```
+- `alpaca_w_system.load_open_orca`: support for open orca datasets with included system prompts, instruct
+  ```json
+  {"system_prompt": "...", "question": "...", "response": "..."}
+  ```
 - `context_qa`: in context question answering from an article
   ```json
   {"article": "...", "question": "...", "answer": "..."}
diff --git a/src/axolotl/prompt_strategies/alpaca_w_system.py b/src/axolotl/prompt_strategies/alpaca_w_system.py
index aacae8739..1b4f50219 100644
--- a/src/axolotl/prompt_strategies/alpaca_w_system.py
+++ b/src/axolotl/prompt_strategies/alpaca_w_system.py
@@ -75,6 +75,20 @@ class SystemDataPrompter(AlpacaPrompter):
         yield res
 
 
+class OpenOrcaPromptTokenizingStrategy(InstructionWSystemPromptTokenizingStrategy):
+    """
+    Tokenizing strategy for OpenOrca datasets
+    """
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str, str]:
+        return (
+            prompt["question"],
+            "",
+            prompt["response"],
+            prompt["system_prompt"],
+        )
+
+
 def load(tokenizer, cfg):
     return InstructionWSystemPromptTokenizingStrategy(
         SystemDataPrompter(PromptStyle.CHAT.value),
@@ -82,3 +96,12 @@ def load(tokenizer, cfg):
         cfg.train_on_inputs,
         cfg.sequence_len,
     )
+
+
+def load_open_orca(tokenizer, cfg):
+    return OpenOrcaPromptTokenizingStrategy(
+        SystemDataPrompter(PromptStyle.INSTRUCT.value),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )