---
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 unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/gemma-2b-it",
    max_seq_length = 1024,
    dtype = torch.float16,
    load_in_4bit = True,
)

# Load LoRA adapter
model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
    lora_dropout=0.05,
    bias="none",
)

# Load parameter
model.load_adapter("kihyun1998/gemma-2b-it-mongodb-lora", adapter_name="default")


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 Park},
  year={2025},
  howpublished={\\url{https://huggingface.co/kihyun1998/gemma-2b-it-mongodb-lora}}
}
```