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Fine-tuning a language model for an Indian use case starts with the task, not the model: decide which language and script it must handle, what users will ask, and how you will judge the results. Then choose a suitable open-weight base model, curate task-matched examples, adapt it with supervised fine-tuning (SFT)—using LoRA if it fits your constraints—and evaluate it on held-out examples from the intended use case. AI4Bharat’s Airavata is a useful Hindi instruction-tuning case study, not a universal recipe.
1. Define the task and the users
“Indian use cases” is not a single model target. Hindi customer support in Devanagari, English-Hindi code-switched chat, a Bengali-language classifier, and translation between Indian languages all require different data and evaluation. Write a short specification before selecting a model.
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- Task: What should the model do—answer questions, follow instructions, classify text, summarize, or translate?
- Language and script: Name each language and the script users actually use. Include romanized text if people are likely to type an Indian language in Latin characters.
- Domain and user phrasing: Identify the subject matter, likely levels of formality, regional vocabulary, spelling variation, and whether prompts will mix languages.
- Deployment constraints: Set expectations for latency, context length, privacy, serving environment, and the compute available for training and inference.
- Failure tolerance: Decide which errors are unacceptable, especially for high-impact advice, personal data, or safety-sensitive requests.
Keep the target narrow enough to evaluate. “Answer common questions about a specific service in Hindi and English” gives you a clearer data and test-set plan than “understand Indian languages.”
2. Choose a base model that fits the target
Compare candidate models against the specification rather than choosing by parameter count or a broad claim of multilingual support. Check the model card and license, access terms, tokenizer behavior, language and script ability, context length, model size, and deployment requirements. Test a small set of representative prompts before committing: a model may handle standard Hindi but struggle with romanized Hindi, code-switching, or your domain vocabulary.
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Airavata illustrates one path, not a default choice for every Indian-language project. AI4Bharat’s 7B Airavata was instruction-fine-tuned on the OpenHathi base model for Hindi assistive tasks; its model card was published on January 26, 2024. Its choice of base and target do not establish suitability for a different language, task, or deployment.
Use “open-weight” when you mean model weights are available. Availability alone does not settle whether the model is open source in every sense, what uses are permitted, or whether the terms fit commercial deployment. Review the specific model’s license and access conditions.
3. Build and audit the training data
Supervised fine-tuning needs examples that show the model what to do: an instruction or user message paired with a suitable response, classification label, or translation target. Match the examples to the real language, script, domain, and conversation style in your specification. More examples do not automatically mean better task performance.
Indic data sources and their trade-offs
AI4Bharat’s IndicLLMSuite documents several ways to build instruction data: combine existing datasets, translate or transliterate English datasets into Indic languages, generate synthetic conversations grounded in India-centric Wikipedia material, and collect prompts through Anudesh. Its repository reports around 74.7 million prompt-response pairs across its IndicAlign collection; that repository-wide figure is not a count of human-written gold examples or a guarantee that any particular subset is clean, licensed for your use, or relevant to your task.
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These construction methods have different risks. Translated examples can sound unnatural or carry errors from the source; transliteration does not necessarily reproduce how users write; synthetic conversations can repeat source-material omissions or mistakes; and a large combined collection can include duplicates or mismatched responses. AI4Bharat’s discussion of its data acknowledges that translation-based data is not ideal and describes challenges in aligning toxic-content handling across linguistic and cultural contexts.
Audit before training
- Verify language and script labels, including romanized and code-switched examples if they are in scope.
- Remove duplicates and near-duplicates, malformed text, broken character encoding, and examples whose answer does not respond to the prompt.
- Review translated and synthetic samples for meaning, fluency, cultural context, and domain correctness; do not assume that a language label guarantees quality.
- Check domain coverage, response style, and safety examples against the users and risks you identified.
- Confirm provenance, license, access conditions, and privacy requirements for each source. Dataset availability does not resolve the rights or privacy status of every included item.
- Keep evaluation examples separate from training and any tuning decisions to reduce the risk of test leakage.
IndicLLMSuite describes cleaning, filtering, and deduplication resources, but using a collection or its tools does not replace an audit for your own task and deployment.
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4. Fine-tune with supervised examples
For an instruction-following assistant, SFT trains the model on example conversations or instruction-response pairs. Hugging Face TRL provides SFT tooling, while PEFT integrations support parameter-efficient adaptation approaches such as LoRA and QLoRA. These approaches update a smaller set of parameters or use memory-saving methods rather than requiring the same full-model training setup; the right choice depends on your model, hardware, implementation, and quality target. The sources here do not establish one universally suitable hardware configuration or training recipe.
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| Setting | Airavata’s reported value |
|---|---|
| LoRA rank | 16 |
| LoRA alpha | 32 |
| LoRA dropout | 0.05 |
| Target modules | q_proj, v_proj, k_proj, down_proj, gate_proj, up_proj |
| Training epochs | 4 |
| Learning rate | 5e-4 |
| Batch size | 128 |
| Precision | bfloat16 |
Those values describe the Airavata experiment, not a result you should expect to reproduce on another base model or dataset. Choose settings for your model and training setup, then use held-out evaluation to determine whether the adaptation helps. A training run completing successfully only shows that the procedure ran; it does not show that responses improved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Preserve the model’s conversation format
When fine-tuning a chat model, format examples in the structure expected by the selected base model. Airavata documents user and assistant role markers and notes that formatting affects generation quality. Other models can use different chat templates, so copy the selected model’s documented format rather than transplanting Airavata’s markers. Inconsistent formatting between training data and inference can undermine instruction-following behavior even when the examples themselves are relevant.
6. Evaluate the behavior you actually need
Use both a held-out set of locally reviewed examples and relevant benchmarks. Benchmarks help with consistent comparison, but none alone establishes quality for every language, domain, or product use case. Keep the test data out of training, compare outputs with the original base model and a reasonable prompted baseline, and inspect errors with fluent speakers or qualified domain reviewers.
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| Need | Possible evaluation evidence | What it does not establish by itself |
|---|---|---|
| Hindi instruction following or language understanding | Hindi Indic NLU and commonsense tasks listed in the IndicInstruct README, plus held-out examples from your own domain | Performance in another language, script, or specialized domain |
| Indic-language generation | Indic NLG tasks listed in the IndicInstruct README, plus human review of target-language responses | Factual correctness or safety for every product use |
| English-Hindi translation | English-Hindi translation evaluations listed for IndicInstruct; IndicTrans2’s IN22 general and conversational subsets are also relevant to translation evaluation | Chat quality or unrelated domain performance |
| Other Indian-language, code-switched, or domain-specific behavior | A held-out test set reflecting the intended language mix, script, users, and domain, reviewed by suitable speakers or experts | Broad performance outside the language and scenarios represented in that test set |
IndicTrans2 lists 22 scheduled Indic languages and supports multiple scripts; its repository includes general and conversational IN22 evaluation subsets. That makes it relevant context for translation work, not a substitute for evaluating a chatbot, classifier, or domain assistant on its own task. AI4Bharat’s overview also reports 251 billion pretraining tokens across 22 languages; the overview was accessed in 2026, its page footer shows © 2024, and it does not independently date that statistic. Pretraining scale is not a measure of how well a fine-tune will perform on your use case.
Review failures and iterate
Track errors by type, such as wrong-language replies, script problems, mistranslation, unsupported claims, poor instruction following, unsafe responses, or failures on code-switched prompts. Compare the fine-tuned model with the base and prompted baseline on the same held-out cases. If it regresses, inspect the data and formatting as well as training settings; if it improves only on familiar examples, check for overlap or leakage. Add or revise examples based on observed failure patterns, then evaluate again without using the test set as training data.
Quick Recap
7. A practical go/no-go checklist
- Proceed when the model’s license and access terms fit, representative training data is available, and you can evaluate the target behavior with held-out examples.
- Pause if language or script labels are unreliable, translated or synthetic examples have not been reviewed, or data rights and privacy conditions are unclear.
- Do not claim success from loss curves, dataset size, or a translation benchmark alone. Require a measured comparison on the intended task and review important failures before deployment.
- Recheck moving parts—model cards, licenses, data terms, software dependencies, and benchmark releases—against the current versions before running a project. The Airavata settings described above belong to its 2024 case study.
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