Create README.md

README.md

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+tags:
+- COBOL generation
+- COBOL-Java translation
+---
+  
+# COBOL-Coder: Domain-Adapted Large Language Models for COBOL Code Generation and Translation
+</div>
+
+## Introduction
+
+COBOL-Coder is a family of domain-adapted LLMs specialized for COBOL code generation and bidirectional COBOL-Java code translation. Built on top of Qwen2.5-Coder, COBOL-Coder addresses the critical gap in LLM capabilities for legacy programming languages.
+
+<!-- 
+## Model Versions
+
+We release XMAiNframe with 7B and 10.5B parameters, including base and instruct models, to the public. XMAiNframe 10.5B is expanded from DeepSeek-Coder 7B by the depth up-scaling method without introducing additional modules or dynamic expert selection methods.
+
+<div align="center">
+
+|            **Model**            |      **Download**  |
+| :-----------------------------: |  :----------------------------------------------------------: |
+|   XMAiNframe-base-7b         | [🤗 HuggingFace](https://https://huggingface.co/Fsoft-AIC/XMAiNframe-base-7b/) |
+| XMAiNframe-instruct-7b    | [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-7b) |
+|     XMAiNframe-base-10.5b     |       [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-base-10.5b) |
+|   XMAiNframe-instruct-10.5b   |   [🤗 HuggingFace](https://huggingface.co/Fsoft-AIC/XMAiNframe-instruct-10.5b) |
+
+</div> -->
+
+
+## Quickstart
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/COBOL-Coder-7B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("Fsoft-AIC/COBOL-Coder-7B-Instruct")
+
+prompt = """Complete the given COBOL code:
+"""
+messages = [
+    {"role": "system", "content": "You are a helpful assistant for COBOL generation."},
+    {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+ 
+outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
+```
+
+## Additional Information
+### Other Resources:
+- Github: https://github.com/COBOL-Coder/COBOL-Coder
+- Paper: https://arxiv.org/abs/2604.03986
+
+
+### Citation Information
+More details can be found in our [paper](https://arxiv.org/abs/2604.03986). 
+
+If you're using COBOL-Coder, please cite using this BibTeX:
+```
+@article{dau2026cobol,
+  title={COBOL-Coder: Domain-Adapted Large Language Models for COBOL Code Generation and Translation},
+  author={Dau, Anh TV and Tan, Shin Hwei and Yang, Jinqiu and Bui, Nghi DQ and Nguyen, Anh Tuan},
+  journal={arXiv preprint arXiv:2604.03986},
+  year={2026}
+}
+```