Start with the product requirement
Before training anything, define what the model must do. A Hindi-English model for customer support has different data and latency requirements from one built for translation, voice assistants or text completion. Decide whether you need:
- A causal language model that generates the next token and powers chat or completion.
- An encoder model for classification, search, moderation or intent detection.
- A translation model that maps Hindi to English and English to Hindi.
- A domain model for sectors such as health, agriculture, finance or government services.
For many Indian startups, fine-tuning or distilling an existing open model is more practical than pretraining from zero. A compact model can reduce inference cost, work on a single GPU or CPU server, and be easier to audit. If the broader goal is to serve first-time internet users, review the design principles in building AI apps for the next billion users in India before choosing model size.
Build a legally usable Hindi-English corpus
Data quality matters more than a large, unexamined web scrape. Assemble a mixture of licensed, public-domain and permissioned data, and record the source, language, domain, licence and collection date for every document. Do not copy private chats or scrape websites in violation of their terms.
Useful sources include:
- Hindi and English monolingual text: licensed books, public government documents, educational material, news with permission and open datasets.
- Parallel text: translation datasets, government terminology and professionally translated documents.
- Code-switched text: consented conversational data, support transcripts and social content where collection and redistribution are permitted.
- Task-specific examples: prompts, ideal answers, refusal examples and domain terminology created or reviewed by native speakers.
India-specific coverage requires more than standard Hindi. Include formal Hindi, colloquial Hindi, Hinglish, regional vocabulary, abbreviations, transliterated Hindi in Latin script, Devanagari punctuation and common English insertions. A useful treatment of data scarcity, script variation and evaluation appears in this guide to low-resource Indic natural language processing.
Clean and label the data
Create a reproducible preprocessing pipeline rather than manually editing files. Remove duplicate documents, boilerplate, navigation text, spam, corrupted Unicode and excessively repetitive content. Keep a raw, immutable copy so every transformation can be audited.
At minimum, label each sample for:
- Script: Devanagari, Latin, mixed or another script.
- Language: Hindi, English, Hinglish or uncertain.
- Domain and source type.
- Quality, toxicity and personally identifiable information.
- Licence and permitted use.
Use Unicode normalisation carefully. Do not discard punctuation, emojis or numerals automatically: they carry meaning in chat, commerce and support settings. Detect and redact phone numbers, addresses, Aadhaar-like identifiers, financial details and other personal data before training. Split documents by source before creating train, validation and test sets; random sentence-level splitting can leak near-duplicates and produce misleading scores.
Design the tokenizer for Indian text
Tokenisation is a major decision. An English-first tokenizer may fragment Devanagari and waste context length, while a Hindi-only tokenizer handles English and Hinglish poorly. Train or adapt a SentencePiece, BPE or unigram tokenizer on a balanced mixture of scripts and domains.
Measure:
- Average tokens per sentence in Hindi, English and Hinglish.
- The proportion of unknown or excessively fragmented tokens.
- Coverage of names, numbers, punctuation and common transliterations.
- Token efficiency at the target context length.
Reserve special tokens only when they serve a clear purpose, such as language or task markers. Language tags can help controlled translation, but excessive tags and artificial formatting may harm natural code-switching. Compare a new tokenizer against the base model’s tokenizer before changing it; resizing embeddings and retraining can erase the benefit of starting from a pretrained checkpoint.
Choose the smallest viable model
For a focused application, a compact decoder model with roughly 100 million to 1 billion parameters may be sufficient. Start from an open-weight multilingual or Indic-capable checkpoint when its licence, training provenance and commercial terms fit your use case. Continue pretraining on clean Hindi-English text, then instruction-tune on task examples. Parameter-efficient methods such as LoRA or QLoRA reduce GPU memory requirements and make experiments affordable.
A practical sequence is:
1. Benchmark an existing model on representative Hindi, English and Hinglish prompts.
2. Continue pretraining on domain text if vocabulary and style are the main gaps.
3. Instruction-tune with high-quality bilingual and code-switched examples.
4. Add preference or rejection data for factuality, safety and response style.
5. Quantise only after measuring quality and latency on target hardware.
Use PyTorch and the Hugging Face ecosystem for most experiments. Track dataset versions, tokenizer versions, random seeds, checkpoints and configuration files. A small model trained transparently is more useful than a larger model whose data and behaviour cannot be reproduced.
Train efficiently
Pack sequences to minimise padding, use mixed precision where hardware supports it, and monitor validation loss separately by language and script. A single overall loss can hide poor Devanagari performance or catastrophic forgetting of English. Use gradient accumulation when GPU memory is limited, and stop training when validation quality plateaus rather than relying on a fixed epoch count.
Keep a held-out test set that contains realistic cases: spelling variation, Romanised Hindi, English nouns inside Hindi sentences, numerals, named entities, slang and domain terminology. Do not use test prompts during prompt-tuning or human review.
Evaluate usefulness, not just perplexity
Perplexity is useful for tracking language modelling progress, but it does not establish that a chatbot is accurate or safe. Evaluate separately for Hindi, English, Hinglish and each script. Include:
- Generation: fluency, instruction following, repetition and coherence.
- Translation: adequacy, terminology and human preference; BLEU alone is insufficient.
- Classification or retrieval: accuracy, macro-F1, recall and calibration.
- Safety: abusive content, privacy leakage, fraud, medical overclaiming and stereotyping.
- Operations: tokens per second, first-token latency, memory use and cost per request.
Use bilingual native-speaker reviewers and a written rubric. Report confidence intervals where possible, publish failure examples, and test whether performance changes across gender, region, socioeconomic context and formal versus colloquial language. For voice products, evaluate the full speech pipeline rather than the text model alone; the architecture considerations in how to build a voice agent are a useful companion.
Deploy with guardrails
Serve the model behind an API with authentication, rate limits, structured logging and prompt-injection protections. Quantisation, batching and response-length limits can materially reduce costs. Cache safe, repeated requests, but never cache sensitive user content without an explicit policy.
Use retrieval for changing facts instead of forcing a small model to memorise them. Cite sources in high-stakes workflows, add escalation to a human, and maintain rollback-ready model versions. For financial, health or legal applications, keep outputs advisory and apply domain-specific review. If privacy is central, consider a private deployment and minimise retained prompts.
A realistic build plan
For a first six-week prototype, spend the first week defining tasks and licences, the second building a deduplicated corpus, the third testing tokenizers and baselines, and the fourth running parameter-efficient fine-tuning. Reserve the final two weeks for human evaluation, red-team testing and deployment measurement. Build a small evaluation set before training so improvements are measurable.
The strongest Hindi-English systems are not necessarily the largest. They are built around representative Indian data, careful script handling, honest evaluation and a deployment plan that matches the product’s constraints. For founders, students and research teams, open-source collaboration and reproducible experiments can be a better investment than training from scratch; Indian student developers building open-source AI offers a relevant path for organising that work.
FAQ
Do I need to train a model from scratch?
Usually not. Begin with a capable open checkpoint, evaluate it on your target data, and use continued pretraining or LoRA fine-tuning where the gaps are clear.
How much data is enough?
There is no universal number. A few million high-quality, diverse tokens can support a narrow adaptation, while general-purpose pretraining requires far more. Quality, deduplication and language balance matter more than a headline document count.
Should Romanised Hindi be included?
Yes, if users type it. Keep it labelled and evaluate it separately from Devanagari Hindi because spelling is inconsistent and tokenisation behaviour differs.
Which metric should I publish?
Report task-specific metrics, human ratings, per-language results, safety failures and latency or cost. Perplexity alone is not a product evaluation.