Update README.md

README.md

@@ -30,4 +30,30 @@ datasets:
 - Dahoas/instruct-synthetic-prompt-responses
 - pankajmathur/WizardLM_Orca
 ---
-This is the second model in the ensemble for the MindsAI @ Tufa Labs team for the ARC Prize 2025 competition.  It was originally based on the CodeT5 model from Salesforce.  It was modified to have 16 layers in the decoder from the original 24 layers.  Testing demonstrated that removing layers was more harmful to performance when removed from the encoder, but was able to fully recover when removing decoder layers.
+This is the second model in the ensemble for the MindsAI @ Tufa Labs team for the ARC Prize 2025 competition.  It was originally based on the CodeT5 model from Salesforce.  It was modified to have 16 layers in the decoder from the original 24 layers.  Testing demonstrated that removing layers was more harmful to performance when removed from the encoder, but was able to fully recover when removing decoder layers.
+
+## ARC Data Formatting
+
+- ARC tasks ship as JSON where each `task_id` contains `train` pairs and `test` inputs; every grid is a rectangular list of lists with integers `0-9`. Dimensions follow the original 1×1–30×30 spec, though the evaluator accepts up to 50×50.
+- Example task payload:
+  ```json
+  {
+    "task_id": {
+      "train": [
+        {"input": [[0,0],[1,1]], "output": [[1,1],[1,1]]}
+      ],
+      "test": [
+        {"input": [[0,0,0],[0,1,0],[0,0,0]]}
+      ]
+    }
+  }
+  ```
+- Model prompts (`prompt` column during training/TTT/inference) are serialized text strings: `solve: train input1 <train_input> output1 <prefix><train_output>. … test tinput1 <test_input> toutput1 `. Each grid token `<train_input>` / `<train_output>` / `<test_input>` is produced by `grid_to_string`, so rows are concatenated digits separated by spaces. Multiple train examples increment the index (`input2`, `output2`, etc.).
+- Prompt example:
+  ```text
+  solve: train input1 000 010 000 output1 11 3 3 10 111 101 111. input2 00 02 output2 5 2 2 20 22 20. test tinput1 0000 0300 0000 0000 toutput1 
+  ```
+- Model targets (`correct_answer` column and expected decoder output before post-processing) follow `output_prefix` semantics: ` {total_chars} {height} {width} {symbols} {row_strings}.` Here `total_chars = height*width + (height - 1)` and `symbols` is the deduplicated sequence of colors as they are first encountered when scanning the board row-major; that rule applies to every output grid we emit (training outputs inside the prompt and the predicted test toutput). Example target string for a 3×3 donut:
+  ```text
+   11 3 3 10 111 101 111.
+  ```