---
base_model: unsloth/gemma-3-270m-it-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/gemma-3-270m-it-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- vcet
- domain-specific
license: apache-2.0
metrics:
- accuracy
---


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

<!-- Provide a quick summary of what the model is/does. -->

## 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]

<!-- Provide the basic links for the model. -->

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

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### 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

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the 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

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[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