Full formTechnology
SFTstands forSupervised Fine-Tuning
Full Form of SFT
What is SFT?
Supervised Fine-Tuning, commonly abbreviated as SFT, is a crucial machine learning technique used to adapt pre-trained large language models to perform specific tasks or align them with desired behaviours. The process involves training a base model on a curated dataset of labelled input-output pairs, where human annotators provide correct responses that the model learns to mimic. In India, SFT has gained tremendous relevance with the rapid growth of AI startups, research labs at premier institutions like IIT Bombay, IISc Bangalore, and IIT Madras, alongside the proliferation of Indic language models such as Navarasa, Airavata, and OpenHathi. Indian companies like Sarvam AI, Krutrim, and several Reliance-backed ventures heavily rely on SFT to build domain-specific chatbots, customer service tools, and government-facing applications in regional languages. The technique is widely discussed in hackathons, Kaggle competitions, and corporate training programmes conducted by NVIDIA, Microsoft, and Google across Indian engineering campuses. For students preparing for GATE Computer Science, UGC-NET, or AI certifications through NPTEL and IIT online programmes, understanding SFT concepts including LoRA and QLoRA variants has become essential for career growth in India's booming artificial intelligence sector.
SFT का फुल फॉर्म
सुपरवाइज्ड फाइन-ट्यूनिंग
Example
The AI research team at IIT Madras spent three months on Supervised Fine-Tuning their Tamil language model to improve conversational accuracy for the government e-governance chatbot.
SFT — frequently asked questions
What is the full form of SFT?
SFT stands for Supervised Fine-Tuning, a machine learning technique used to train pre-trained AI models on labelled datasets for specific tasks.
How is Supervised Fine-Tuning different from RLHF?
SFT uses labelled input-output pairs created by humans to teach a model directly, while RLHF (Reinforcement Learning from Human Feedback) uses reward signals based on human preferences to optimise model behaviour iteratively.
Why is SFT important for Indian language AI models?
SFT helps adapt English-trained foundation models to understand and respond accurately in Indian languages like Tamil, Hindi, Bengali, and Telugu, making AI more accessible across India's diverse linguistic landscape.