Fine-Tuning Open Source LLMs for Indian Business Context
Guide to fine-tuning Llama, Mistral, and other open-source LLMs on Indian business data. Covers LoRA/QLoRA techniques, dataset preparation for Indian languages, domain-specific fine-tuning (legal, financial, medical), and evaluation benchmarks.
How do you fine-tune open source LLMs for Indian business needs?
Fine-tuning for Indian business involves: 1) Selecting base model (Llama 3, Mistral, Gemma), 2) Preparing domain-specific datasets in Hindi/English/regional languages, 3) Using LoRA/QLoRA for efficient fine-tuning on limited GPUs, 4) Training on industry-specific terminology and compliance requirements, 5) Evaluating with Indian business benchmarks. Boolean & Beyond has fine-tuned models for banking (RBI terminology), legal (Indian law), and manufacturing (industry jargon) achieving 40-60% better accuracy than base models.
Why Fine-Tune Open Source LLMs for Indian Business
Generic large language models like GPT-4 and Claude are powerful but expensive at scale and often lack deep understanding of Indian business context. Fine-tuning open source models like Llama 3, Mistral, or Qwen on your own domain data solves both problems: it cuts costs dramatically and aligns the model with Indian regulations, languages, and business practices.
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