Create README.md

README.md

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+---
+license: mit
+---
+# Reflection Model Outputs
+
+This repository contains model output results from various LLMs across multiple tasks and configurations.
+
+## 📂 Dataset Structure
+
+We have 3 runs of data, and all files are organized under the main directory:
+- `EssentialAI/reflection_model_outputs_run1/`
+- `EssentialAI/reflection_model_outputs_run2/`
+- `EssentialAI/reflection_model_outputs_run3/`
+
+Within this, you will find results grouped by **model architecture and checkpoint size**, including:
+
+- `OLMo-2 7B`
+- `OLMo-2 13B`
+- `OLMo-2 32B`
+- `Qwen2.5 0.5B`
+- `Qwen2.5 3B`
+- `Qwen2.5 7B`
+- `Qwen2.5 14B`
+- `Qwen2.5 32B`
+- `Qwen2.5 72B`
+
+Each model folder contains outputs from multiple task setups.
+
+Example task folders include:
+
+| Original Task Folder      | Adversarial Task Folder     |
+|---------------------------|-----------------------------|
+| `bbh_cot_fewshot`         | `bbh_adv`                   |
+| `cruxeval_i`              | `cruxeval_i_adv`            |
+| `cruxeval_o`              | `cruxeval_o_adv`            |
+| `gsm8k-platinum_cot`      | `gsm8k-platinum_adv`        |
+| `gsm8k_cot`               | `gsm8k_adv`                 |
+| `triviaqa`                | `triviaqa_adv`              |
+
+---
+
+## 💾 How to Download the Dataset
+
+To download the entire dataset locally using the Hugging Face Hub, run the following Python snippet with the run results you're interested in:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(
+    repo_id="EssentialAI/reflection_model_outputs_run1",
+    repo_type="dataset",
+    local_dir="reflection_model_outputs_run1"
+)
+```