--- 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).