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README.md

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+---
+license: mit
+library_name: transformers
+---
+# CodeGenModel
+
+<!-- markdownlint-disable first-line-h1 -->
+<!-- markdownlint-disable html -->
+<!-- markdownlint-disable no-duplicate-header -->
+
+<div align="center">
+  <img src="figures/fig1.png" width="60%" alt="CodeGenModel" />
+</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
+
+CodeGenModel is specialized for code generation tasks. This model has been selected as the best checkpoint based on code generation benchmark performance.
+
+<p align="center">
+  <img width="80%" src="figures/fig3.png">
+</p>
+
+The model demonstrates outstanding performance in code-related tasks while maintaining strong general capabilities.
+
+## 2. Evaluation Results
+
+### Comprehensive Benchmark Results
+
+<div align="center">
+
+| | Benchmark | CodeModel-v1 | CodeModel-v2 | CodeGenModel |
+|---|---|---|---|---|
+| **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.550 |
+| | Logical Reasoning | 0.789 | 0.801 | 0.819 |
+| | Common Sense | 0.716 | 0.702 | 0.736 |
+| **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.700 |
+| | Question Answering | 0.582 | 0.599 | 0.607 |
+| | Text Classification | 0.803 | 0.811 | 0.828 |
+| | Sentiment Analysis | 0.777 | 0.781 | 0.792 |
+| **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.650 |
+| | Creative Writing | 0.588 | 0.579 | 0.644 |
+| | Dialogue Generation | 0.621 | 0.635 | 0.767 |
+| | Summarization | 0.745 | 0.755 | 0.767 |
+| **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.804 |
+| | Knowledge Retrieval | 0.651 | 0.668 | 0.676 |
+| | Instruction Following | 0.733 | 0.749 | 0.758 |
+| | Safety Evaluation | 0.718 | 0.701 | 0.739 |
+
+</div>
+
+### Overall Performance Summary
+CodeGenModel achieves top performance on code generation tasks among all checkpoints in this training run.
+
+## 3. How to Use
+
+### Installation
+```bash
+pip install transformers
+```
+
+### Quick Start
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("CodeGenModel")
+tokenizer = AutoTokenizer.from_pretrained("CodeGenModel")
+```
+
+## 4. License
+This repository is licensed under the [MIT 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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