Instructions to use SandeepCodez/gemma-270-it-vcet-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandeepCodez/gemma-270-it-vcet-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-3-270m-it-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "SandeepCodez/gemma-270-it-vcet-lora")

Transformers

How to use SandeepCodez/gemma-270-it-vcet-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SandeepCodez/gemma-270-it-vcet-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SandeepCodez/gemma-270-it-vcet-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SandeepCodez/gemma-270-it-vcet-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandeepCodez/gemma-270-it-vcet-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandeepCodez/gemma-270-it-vcet-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandeepCodez/gemma-270-it-vcet-lora

SGLang

How to use SandeepCodez/gemma-270-it-vcet-lora with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SandeepCodez/gemma-270-it-vcet-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandeepCodez/gemma-270-it-vcet-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SandeepCodez/gemma-270-it-vcet-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandeepCodez/gemma-270-it-vcet-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use SandeepCodez/gemma-270-it-vcet-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandeepCodez/gemma-270-it-vcet-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandeepCodez/gemma-270-it-vcet-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandeepCodez/gemma-270-it-vcet-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SandeepCodez/gemma-270-it-vcet-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use SandeepCodez/gemma-270-it-vcet-lora with Docker Model Runner:
```
docker model run hf.co/SandeepCodez/gemma-270-it-vcet-lora
```

Model Card for gemma-270-it-vcet-lora

Model Details

Model Description

This model is a domain-specific conversational AI fine-tuned on custom data related to VCET College, Madurai. Built on top of unsloth/gemma-3-270m-it-unsloth-bnb-4bit, it uses LoRA and PEFT for efficient adaptation. The model is designed to answer queries about campus life, academics, departments, events, and administrative processes at VCET.

Developed by: SandeepCodez.
Funded by [optional]: Self-funded.
Shared by [optional]: SandeepCodez
Model type: Causal Language Model (Text Generation).
Language(s) (NLP): English (with contextual Tamil understanding).
License: Apache 2.0.
Finetuned from model [optional]: unsloth/gemma-3-270m-it-unsloth-bnb-4bit

Model Sources [optional]

Repository: https://github.com/SandeepCodez
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

Answering VCET-related questions

Assisting students with academic and campus queries

Automating college FAQs

Supporting chatbot integration for VCET platforms

[More Information Needed]

Downstream Use [optional]

Integration into college ERP systems

Enhancing virtual assistants for student support

Embedding in mobile apps or websites

[More Information Needed]

Out-of-Scope Use

General-purpose text generation outside VCET context

Legal, medical, or financial advice

High-stakes decision-making without human oversight

[More Information Needed]

Bias, Risks, and Limitations

May reflect institutional bias from VCET sources

Limited generalization outside VCET domain

Not suitable for sensitive or critical applications

[More Information Needed]

Recommendations

Use in supervised environments

Periodic updates to dataset recommended

Human validation for factual accuracy advised

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "SandeepCodez/gemma-270-it-vcet-lora" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("What are the placement statistics for VCET Madurai?", return_tensors="pt") outputs = model.generate(**inputs) print(tokenizer.decode(outputs[0]))

Training Details

Training Data

Custom dataset created by the developer, including:

VCET brochures

Departmental documents

Student interviews

Campus FAQs

Event archives

[More Information Needed]

Training Procedure

Preprocessing [optional]

Cleaned and structured into JSONL format

Tokenized using Gemma tokenizer

Filtered for relevance and clarity

Training Hyperparameters

Training regime: bf16 mixed precision

Epochs: 3

Batch Size: 16

Learning Rate: 2e-4

Frameworks: PEFT 0.17.1, TRL, Unsloth

Speeds, Sizes, Times [optional]

Training Time: ~3 hours

Dataset Size: ~10,000 samples

Model Size: 270M parameters

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.17.1

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Model tree for SandeepCodez/gemma-270-it-vcet-lora

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it-unsloth-bnb-4bit

Adapter

(12)

this model

Paper for SandeepCodez/gemma-270-it-vcet-lora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 49