Apply for AI Grants India

A Hindi model can score well on a general benchmark and still fail on the inputs that matter in production: code-mixed Hindi-English, Devanagari spelling variation, regional vocabulary, noisy user text, or domain-specific terminology. Benchmarking should therefore compare your fine-tuned model with credible baselines under a fixed, representative evaluation protocol—not just produce a single accuracy number.

Hugging Face’s Model Comparison Playground (MCP) can help organise side-by-side comparisons, but it should be treated as one part of the evaluation stack. Use it to inspect comparable models and results, then validate important findings locally with the exact dataset, prompt format, decoding settings, and hardware your application will use.

Define the task before opening MCP

Start by writing down what the model must do. A classification model, instruction-tuned chatbot, translation system, and summariser require different datasets and metrics. Record:

• Task: classification, extraction, question answering, translation, summarisation, or open-ended generation.
• Input format: Devanagari Hindi, Romanised Hindi, Hindi-English code-mixing, or a combination.
• Output contract: label, JSON, short answer, translation, or free-form response.
• Target users and domain: education, public services, finance, healthcare, customer support, or another setting.
• Operational constraints: latency, memory, throughput, context length, and inference cost.

If you are still selecting a base model, compare options in the context of open-source small language models for Hindi. A smaller model with better Hindi coverage and lower latency may be more useful than a larger model with a higher generic score.

Build a representative Hindi evaluation set

Do not benchmark only on the examples used during fine-tuning. Create a held-out test set that reflects real traffic and keep it inaccessible during training and prompt development. For a useful Hindi evaluation set, include:

• Devanagari text with common spelling and punctuation variation.
• Romanised Hindi, where relevant to your users.
• Hindi-English code-mixed queries such as “loan ka status kaise check karun?”
• Formal, conversational, and regional phrasing.
• Short queries, long instructions, incomplete sentences, and noisy text.
• Names, dates, currency, addresses, numbers, and domain terminology.
• Safety-sensitive or ambiguous requests appropriate to your use case.

Use separate development, test, and—if possible—challenge sets. The challenge set should contain cases that expose known weaknesses rather than merely repeating the distribution of the main test set. Remove duplicates and near-duplicates across training and evaluation data; leakage can make a weak model look strong.

For a model adapted to an Indian regional-language workflow, document the source, licence, collection method, annotator instructions, dialect coverage, and label agreement. These details make results reproducible and help reviewers understand what the score actually represents.

Prepare the model and baselines

Publish or record the exact model revision, tokenizer, chat template, quantisation method, and inference library. Load the model locally for a controlled check:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "org-or-user/your-hindi-model"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision="main")
model = AutoModelForCausalLM.from_pretrained(model_id, revision="main")

For sequence classification, use AutoModelForSequenceClassification; for translation or summarisation, use the appropriate sequence-to-sequence class. Confirm that the tokenizer handles Devanagari correctly and that the model’s expected prompt format matches the format used during fine-tuning.

Benchmark at least three references where possible:

• The original base model without your fine-tuning.
• A strong Hindi or multilingual open model of a similar size.
• A simple non-LLM baseline, such as a majority classifier or retrieval system, when applicable.

If your fine-tuning process is not yet stable, review best practices for fine-tuning LLMs on custom data before interpreting benchmark gains.

Use Hugging Face MCP for comparison

Open the Hugging Face Model Comparison Playground and select models that support the same task and evaluation setup. Add your fine-tuned model if the interface supports its architecture, task, and required access settings. Do not assume that every model can be compared fairly: differences in tokenizer, context window, system prompt, or tool support can affect results.

For each comparison, record:

1. Model name, revision, parameter size, and quantisation.
2. Dataset and split used.
3. Prompt or input template.
4. Decoding settings, including temperature, top-p, maximum tokens, and stop sequences.
5. Hardware, batch size, and runtime configuration.
6. Metrics, sample count, and any filtering or retries.

MCP results are most useful when the compared models have genuinely comparable inputs and outputs. Treat leaderboard-style results as directional if the evaluation harness does not expose all of these controls.

Choose metrics that match the application

For structured tasks, report more than one metric:

• Accuracy: useful for balanced single-label classification, but misleading with skewed classes.
• Macro-F1: gives minority classes equal weight and is often better for imbalanced Hindi datasets.
• Precision and recall: important when false positives and false negatives have different costs.
• Exact match and token-level F1: useful for extractive question answering.
• ROUGE: a rough signal for summarisation, but not a substitute for human review.
• BLEU or chrF: useful for translation; chrF can be informative for morphologically rich language output.
• Perplexity: helpful for language modelling, but not a direct measure of instruction-following quality.

For generative models, add a human or rubric-based review. Score factuality, instruction adherence, fluency, relevance, harmful content, and preservation of meaning. Use bilingual reviewers where possible, and separate language quality from factual correctness. A fluent Hindi answer can still contain a serious factual error.

Analyse errors, not only averages

After MCP produces a comparison, export or reproduce the evaluation locally and inspect failures. Break down results by script, input length, code-mixing, domain, dialect, and task category. A model with a higher overall score may perform poorly on minority categories that are important to your product.

Create an error taxonomy such as:

• Misunderstanding colloquial or Romanised Hindi.
• Hallucinated facts or unsupported citations.
• Incorrect handling of negation, dates, numbers, or named entities.
• Devanagari spelling and morphological errors.
• Failure to follow JSON or other output constraints.
• Unsafe, overconfident, or culturally inappropriate responses.

Save representative failures with model version and decoding settings. This turns benchmarking into a fine-tuning backlog rather than a one-time report. For applications involving multimodal inputs, compare language performance separately from vision performance; related guidance on open-source vision-language models for Indian languages can help define that boundary.

Measure production readiness

Quality is only one dimension. Measure median and p95 latency, tokens per second, peak memory, throughput, cold-start time, and cost per request. Repeat tests on the hardware you plan to deploy. If the model must run on edge hardware or modest cloud instances, assess compression and runtime options using this AI model optimisation for mobile devices guide.

Run a small safety evaluation covering prompt injection, personal data, abusive language, medical or financial advice, and refusal behaviour relevant to your product. Keep safety results separate from general language quality so a single composite score does not hide important risks.

Report results reproducibly

A credible benchmark report should include the model revision, dataset licence and size, split construction, prompt template, decoding parameters, hardware, software versions, metrics, confidence intervals or repeated-run variation, and known limitations. Publish the evaluation script and anonymised examples when licensing and privacy allow.

Most importantly, state what the benchmark does not measure. A test set built from formal Devanagari news cannot establish performance for Romanised customer messages. A high BLEU score cannot prove factuality. Transparent limits make the benchmark more useful to builders, funders, and users.

Practical checklist

Before accepting a result, confirm that you have:

• A leakage-checked Hindi test set representative of actual users.
• At least one unfine-tuned baseline and one comparable reference model.
• Task-appropriate automatic metrics plus human review for generation.
• Breakdown scores for code-mixing, script, domain, and difficult cases.
• Latency, memory, throughput, and cost measurements.
• A documented model revision, prompt, harness, and decoding configuration.
• A list of failure cases and a plan for the next training or data iteration.

Benchmarking a fine-tuned Hindi model with Hugging Face MCP is most valuable when it supports a disciplined decision: deploy, improve the data, change the model, or narrow the product claim. Use MCP to accelerate comparison, but rely on a reproducible, India-relevant evaluation protocol to decide whether the model is ready.