base_model: google/gemma-3-270m-it library_name: peft pipeline_tag: text-generation tags: - base_model:adapter: google/gemma-3-270m-it - lora - sft - transformers - trl - unsloth - vcet - domain-specific license: apache-2.0 metrics: - accuracy
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
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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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
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
BibTeX:
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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Framework versions
- PEFT 0.17.1
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