metadata
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
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:
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