kihyun1998
/

gemma-2b-it-mongodb-lora

@@ -1,202 +1,131 @@
 ---
-base_model: unsloth/gemma-2b-bnb-4bit
 library_name: peft
 ---
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-### Framework versions
-- PEFT 0.14.0

 ---
+base_model: unsloth/gemma-2b-it
 library_name: peft
+tags:
+- text-to-mongodb
+- LoRA
+- instruction-tuning
+- mongodb
+- gemma
+license: mit
+language:
+- en
 ---
+# 🧠 Gemma 2B - MongoDB Query Generator (LoRA)
+This is a LoRA fine-tuned version of `unsloth/gemma-2b-it` that converts natural language instructions into **MongoDB query strings** like:
+```js
+db.users.find({ "isActive": true, "age": { "$gt": 30 } })
+```
+The model is instruction-tuned to support a text-to-query use case for MongoDB across typical collections like `users`, `orders`, and `products`.
+---
+## ✨ Model Details
+- **Base model**: [`unsloth/gemma-2b-it`](https://huggingface.co/unsloth/gemma-2b-it)
+- **Fine-tuned with**: LoRA (4-bit quantized)
+- **Framework**: [Unsloth](https://github.com/unslothai/unsloth) + PEFT
+- **Dataset**: Synthetic instructions paired with MongoDB queries (300+ examples)
+- **Use case**: Text-to-MongoDB query generation
+---
+## 📦 How to Use
+```python
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+base = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it", load_in_4bit=True, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2b-it")
+model = PeftModel.from_pretrained(base, "kihyun1998/gemma-2b-mongodb-lora")
+prompt = """### Instruction:
+Convert to MongoDB query string.
+### Input:
+Collection: users
+Fields:
+- name (string)
+- age (int)
+- isActive (boolean)
+- country (string)
+Question: Show all active users from Korea older than 30.
+### Response:
+"""
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+output = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+---
+## 💡 Example Output
+```js
+db.users.find({ "isActive": true, "country": "Korea", "age": { "$gt": 30 } })
+```
+---
+## 📚 Intended Use
+- Converting business-friendly questions into executable MongoDB queries
+- Powering internal dashboards, query builders, or no-code tools
+- Works best on structured fields and simple query logic
+### Out-of-scope:
+- Complex joins or aggregation pipelines
+- Nested or dynamic schema reasoning
+---
+## 📊 Training Details
+- LoRA rank: 16
+- Epochs: 3
+- Dataset: 300+ synthetic natural language → MongoDB query pairs
+- Training hardware: Google Colab (T4 GPU)
+---
+## 🚧 Limitations
+- Model assumes collection and fields are already known (RAG context required)
+- May hallucinate field names not present in context
+- Limited handling of advanced MongoDB features like `$lookup`, `$aggregate`
+---
+## 🧾 License
+The base model is under [Gemma license](https://ai.google.dev/gemma#license).
+This LoRA adapter inherits the same conditions.
+---
+## 🧑‍💻 Author
+- 🐱 [@kihyun1998](https://huggingface.co/kihyun1998)
+- 💬 Questions? Open an issue or contact via Hugging Face.
+---
+## 🏁 Citation
+```bibtex
+@misc{kihyun2025mongodb,
+  title={Gemma 2B MongoDB Query Generator (LoRA)},
+  author={Kihyun Lee},
+  year={2025},
+  howpublished={\\url{https://huggingface.co/kihyun1998/gemma-2b-mongodb-lora}}
+}
+```