README.md

---
license: gemma
base_model: google/gemma-2-2b-it
tags:
  - gemma2
  - fine-tuned
  - qlora
  - technical-assistant
  - aws
  - security
  - finance
language:
  - en
library_name: transformers
pipeline_tag: text-generation
---

# gemma2-2b-technical-assistant

Fine-tuned Gemma 2 2B IT model for personalized technical assistance.

## Model Description

This model is a QLoRA fine-tuned version of `google/gemma-2-2b-it`, specialized for:
- AWS cloud security guidance
- FastAPI/Python backend development
- Finance application development
- Kubernetes workload management
- ISO 27001:2022 compliance

## Training Details

- **Base Model:** google/gemma-2-2b-it
- **Fine-tuning Method:** QLoRA (4-bit quantization)
- **LoRA Rank:** 16
- **LoRA Alpha:** 32
- **Training Epochs:** 5
- **Hardware:** Google Colab T4 GPU

## Usage

### Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("satejh/gemma2-2b-technical-assistant")
tokenizer = AutoTokenizer.from_pretrained("satejh/gemma2-2b-technical-assistant")

prompt = "<start_of_turn>user\nWhat database should I use?<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))
```

### Ollama

Download the GGUF file and Modelfile from this repo, then:
```bash
ollama create gemma2-2b-technical-assistant -f Modelfile
ollama run gemma2-2b-technical-assistant
```

## Intended Use

This model is designed as a personalized technical assistant with:
- Security-first approach
- Read-only database interactions
- Direct, actionable responses
- AWS and Kubernetes expertise