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

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

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