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NCU Smart LLM (phi2-ncu) — Smart LLM Fine-tuned for NCU Tasks

NCU Logo

A lightweight, instruction-tuned version of Microsoft's Phi-2, customized for use cases and conversations related to The NorthCap University (NCU), India. Fine-tuned using LoRA on 1,098 high-quality examples, it's optimized for academic, administrative, and smart campus queries.

Highlights

Base Model: microsoft/phi-2 (2.7B parameters)
Fine-tuned Using: Low-Rank Adaptation (LoRA) + PEFT + Hugging Face Transformers
Dataset: University questions, FAQs, policies, academic support queries, smart campus data
Training Environment: Google Colab (T4 GPU), 4 epochs, batch size 1, no FP16
Final Format: Full model weights (.safetensors) + tokenizer

Model Access

Platform	Access Method
Hugging Face	phi2-ncu-model
Hugging Face Space	Live Chatbot Demo
Ollama (Offline)	`ollama create phi2-ncu -f Modelfile` (self-hosted only)

Try It Online

Gradio Web Chat (Hugging Face Space) (Runs Slow because of free CPU Hardware)

👉 Visit: https://huggingface.co/spaces/pranav2711/phi2-ncu-chat-space

Built using Gradio, deployed on Hugging Face Spaces

How to Use Locally (Hugging Face Transformers)

pip install transformers accelerate peft

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig

# Load adapter config
adapter_path = "pranav2711/phi2-ncu-model"
base_model = "microsoft/phi-2"

# Load tokenizer and base
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")

# Load fine-tuned adapter
model = PeftModel.from_pretrained(model, adapter_path)

# Inference
input_prompt = "### Question:\nHow can I apply for re-evaluation at NCU?\n\n### Answer:"
inputs = tokenizer(input_prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

How to Use with Ollama (Offline)

This works only locally via ollama create and not yet shareable as public Ollama model hub is restricted.

Folder Structure

phi2-ncu/
├── Modelfile
└── model/
    ├── model.safetensors
    ├── config.json
    ├── tokenizer.json
    ├── tokenizer_config.json
    ├── vocab.json
    ├── merges.txt

Steps

bash

ollama create phi2-ncu -f Modelfile
ollama run phi2-ncu

Example Dataset Format (Used for Training)

json

{
  "instruction": "How do I get my degree certificate?",
  "input": "I'm a 2023 BTech passout from CSE at NCU.",
  "output": "You can collect your degree certificate from the admin block on working days between 9AM and 4PM. Carry a valid ID proof."
}

Formatted as:

### Question:
How do I get my degree certificate?
I'm a 2023 BTech passout from CSE at NCU.

### Answer:
You can collect your degree certificate...

Training Strategy

Used LoRA with rank=8, alpha=16
Tokenized to max length = 512
Used Trainer with fp16=False to avoid CUDA AMP issues
Batch size = 1, Epochs = 4
Trained on Google Colab (T4), saving final full weights

License

Apache 2.0

About NCU

The NorthCap University, Gurugram (formerly ITM University), is a multidisciplinary university with programs in engineering, management, law, and sciences.

This model was created as part of a research initiative to explore AI for academic services, campus automation, and local LLM deployments.

Contribute

Have better FAQs or data? Want to train on your college corpus? Fork the repo or raise a PR at:

👉 https://github.com/pranav2711/ncu-smartllm