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README.md

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-license: mit
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+---
+license: mit
+---
+
+# 🧠 `qwen2.5-md-finetuned`
+
+## Model Overview
+
+`qwen2.5-md-finetuned` is a fine-tuned version of the **Qwen2.5-Medium** model, optimized for improved performance on domain-specific or task-specific data. This model leverages the powerful multilingual and multitask capabilities of the base Qwen2.5 architecture and is adapted further using Low-Rank Adaptation (LoRA) techniques for efficient fine-tuning.
+
+> ✅ **Base Model:** [Qwen2.5-Medium](https://huggingface.co/Qwen/Qwen2.5-Medium)
+> 🛠️ **Fine-Tuned By:** [adi2606](https://huggingface.co/adi2606)
+> 📜 **License:** MIT
+> 🧱 **Adapter Format:** `adapter_model.safetensors` (LoRA)
+
+---
+
+## 📌 Use Cases
+
+This model is best suited for:
+
+* Custom conversational agents
+* Code or documentation assistants
+* Knowledge-based QA systems
+* Any application benefiting from Qwen2.5’s capabilities but requiring domain-specific fine-tuning
+
+---
+
+## 🔧 Fine-Tuning Details
+
+* **Technique:** Parameter-efficient fine-tuning using LoRA
+* **Adapter Config:** See `adapter_config.json`
+* **Tokenizer:** Includes full tokenizer configuration (`tokenizer_config.json`, `vocab.json`, `merges.txt`)
+* **Additional Tokens:** `added_tokens.json` and `special_tokens_map.json` for enhanced compatibility with downstream applications
+
+---
+
+## 💾 Files
+
+| Filename                    | Description                          |
+| --------------------------- | ------------------------------------ |
+| `adapter_model.safetensors` | LoRA adapter weights                 |
+| `adapter_config.json`       | Adapter configuration for inference  |
+| `tokenizer_config.json`     | Tokenizer configuration              |
+| `tokenizer.json`            | Pre-tokenized vocabulary             |
+| `vocab.json`                | Vocabulary JSON                      |
+| `merges.txt`                | Merge rules for BPE tokenizer        |
+| `special_tokens_map.json`   | Special tokens mapping               |
+| `added_tokens.json`         | Custom added tokens                  |
+| `chat_template.jinja`       | Custom chat template (if applicable) |
+
+---
+
+## ✅ How to Use
+
+You can load this adapter with the base Qwen2.5-Medium model using `peft`:
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Medium", device_map="auto", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("adi2606/qwen2.5-md-finetuned", trust_remote_code=True)
+model = PeftModel.from_pretrained(base_model, "adi2606/qwen2.5-md-finetuned")
+```
+
+---
+
+## 📈 Performance
+
+> (Optional section)
+> If you have evaluation metrics or benchmark results, they can be added here. Example:
+
+* Domain accuracy: 89.3%
+* BLEU/ROUGE/F1 scores if applicable
+
+---
+
+## 📚 Citation
+
+If you use this model in your work, please consider citing it:
+
+```bibtex
+@misc{adi2606qwen25md,
+  author = {adi2606},
+  title = {qwen2.5-md-finetuned},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/adi2606/qwen2.5-md-finetuned}},
+}
+```
+
+---
+
+## 🤝 Contributions
+
+If you find issues or would like to contribute improvements to the model or tokenizer, feel free to open a pull request or discussion on the [model repository](https://huggingface.co/adi2606/qwen2.5-md-finetuned).
+