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README.md

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+---
+license: apache-2.0
+library_name: transformers
+---
+# EarlyCheckpoint
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="figures/fig1.png" width="60%" alt="EarlyCheckpoint" />
+</div>
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="LICENSE" style="margin: 2px;">
+    <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## 1. Introduction
+
+EarlyCheckpoint is the first saved checkpoint from our training run, captured at the very beginning of training. It serves as a baseline for comparing training progress.
+
+<p align="center">
+  <img width="80%" src="figures/fig3.png">
+</p>
+
+This model represents the initial state of training and is useful for ablation studies and understanding training dynamics.
+
+## 2. Model Information
+
+| Property | Value |
+|---|---|
+| Architecture | BERT |
+| Training Step | step_100 |
+| License | Apache-2.0 |
+
+## 3. How to Use
+
+```python
+from transformers import AutoModel, AutoTokenizer
+
+model = AutoModel.from_pretrained("EarlyCheckpoint-v1")
+tokenizer = AutoTokenizer.from_pretrained("EarlyCheckpoint-v1")
+```
+
+## 4. License
+This model is licensed under the [Apache-2.0 License](LICENSE).
+
+## 5. Contact
+Open an issue on our GitHub for questions.

config.json

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+{
+    "model_type": "bert",
+    "architectures": ["BertModel"]
+  }

pytorch_model.bin

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