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Why this benchmark matters

Nepali evaluation is easy to get wrong. A model may appear strong because a test set is small, prompts are inconsistent, or tokenisation handles Devanagari poorly. IndicGenBench should be treated as an evaluation protocol, not a single score: identify the relevant Nepali tasks, use the official splits and prompts, preserve generation settings, and report enough detail for another team to reproduce the run.

This workflow uses the Hugging Face Hub and transformers for model access, while leaving dataset loading and scoring to the IndicGenBench implementation specified by its documentation or repository. Interfaces can change, so verify the current task names, configuration files, and metric scripts before running a published comparison. For broader context, compare your setup with this Indian language LLM benchmark datasets guide and the practical framework for benchmarking multilingual LLMs in India.

1. Define the evaluation before writing code

Start with a short evaluation plan:

• Language: Nepali, including the exact script and dialect or domain represented.
• Task: generation, translation, question answering, classification, or another supported IndicGenBench task.
• Model class: causal language model, encoder-decoder model, or task-specific checkpoint.
• Evaluation mode: zero-shot, few-shot, instruction-tuned, or fine-tuned.
• Metrics: use the benchmark’s official metrics first; add human review for quality dimensions automated scores miss.
• Comparison set: record model versions, parameter counts where available, context length, and decoding settings.

Do not compare a chat-tuned model using few-shot prompts with a base model using direct labels and call the result a clean leaderboard comparison. Keep the protocol identical, or clearly label the runs as exploratory.

2. Create a reproducible Hugging Face environment

Use an isolated environment and pin versions. A typical starting point is:

python -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install torch transformers datasets evaluate accelerate sentencepiece

Install IndicGenBench exactly as its current documentation requires. If it is distributed as a repository rather than a packaged release, record the commit hash. Also capture:

• Python and PyTorch versions
• CUDA and GPU details
• Hugging Face model revision or commit
• Dataset revision and local file checksums
• Random seeds
• Batch size, precision, and maximum input length

This matters particularly for Nepali, where small preprocessing changes—Unicode normalisation, whitespace handling, punctuation, or prompt translation—can affect scores. A simple requirements.txt, run log, and JSON configuration file will save more time than manually reconstructing an experiment later.

3. Choose and inspect the Nepali checkpoint

Search the Hugging Face Model Hub for a checkpoint that supports Nepali and matches the task. Check its model card rather than relying on the model name. Look for training languages, licence, intended use, tokenizer details, context window, and known limitations.

For a causal model, a minimal loading pattern is:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "org-or-user/model-name"
tokenizer = AutoTokenizer.from_pretrained(model_id, revision="main")
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    revision="main",
    torch_dtype="auto",
    device_map="auto",
)
model.eval()

Use AutoModelForSeq2SeqLM for an encoder-decoder checkpoint and the task-specific class for classification or token labelling. Confirm that the tokenizer preserves Nepali text correctly. Test strings containing Devanagari vowel signs, danda punctuation, numerals, mixed English, and zero-width characters. Never silently replace unknown characters or normalise benchmark inputs differently from the official pipeline.

If you are adapting a model rather than only evaluating it, document the training data and avoid contamination. The local-language fine-tuning guide for Nepali is useful for separating fine-tuning decisions from evaluation decisions.

4. Load the official IndicGenBench data

Use the benchmark’s official loader, configuration, and split names wherever possible. Avoid downloading a similarly named dataset from an unverified mirror. Before inference, inspect a sample and verify:

• the language field is Nepali;
• the prompt and reference fields are mapped correctly;
• no training examples have entered the test split;
• examples are not duplicated after preprocessing;
• references retain the original Unicode text;
• task instructions are applied consistently.

If IndicGenBench exposes Hugging Face datasets integration, the pattern may look like this, but treat it as illustrative rather than a guaranteed current API:

from datasets import load_dataset

data = load_dataset("benchmark-org/indicgenbench", "nepali-task")
print(data)
print(data["test"][0])

Record the dataset version and configuration in your experiment manifest. If the benchmark supplies prompts, use them verbatim for the primary run. You can run translated or instruction-improved prompts as a separate ablation, not as a replacement for the standard condition.

5. Run controlled inference

Set generation parameters explicitly. For deterministic evaluation, begin with greedy decoding or the benchmark’s prescribed settings. Do not allow sampling defaults to vary between models.

import torch

prompt = "नेपालीमा उत्तर दिनुहोस्: नेपालको राजधानी कुन हो?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

prediction = tokenizer.decode(output[0], skip_special_tokens=True)
print(prediction)

For a benchmark loop, save each example’s identifier, prompt, raw output, cleaned output, model revision, and generation configuration. Keep raw outputs untouched. Scoring scripts sometimes require a particular answer format, but cleaning should be transparent and applied identically across models.

For large runs, use batching, accelerate, or a compatible inference server. Measure throughput and memory alongside quality. A model that scores marginally higher but requires substantially more compute may not be the best choice for an Indian deployment.

6. Score, validate, and analyse errors

Run the official IndicGenBench evaluator first. Report aggregate scores and, where available, per-task and per-category results. For Nepali, add a small manual audit covering:

• factuality and answer completeness;
• grammaticality and natural phrasing;
• script preservation;
• code-mixing and named entities;
• respect for instruction and output format;
• hallucinations and unsafe or culturally inappropriate content.

Automatic metrics can reward lexical overlap while missing a correct paraphrase, or penalise valid Nepali wording. For translation and generation, consider human evaluation with bilingual reviewers and a written rubric. Report reviewer count, sampling method, and agreement where feasible.

Inspect failures by category rather than reading only the average score. Useful slices include short versus long prompts, formal versus conversational Nepali, numerals, dates, named entities, and examples containing English or regional references. This turns a benchmark into an engineering backlog.

7. Publish a result others can trust

A useful report includes the exact model ID and revision, dataset revision, task configuration, prompt template, decoding parameters, hardware, software versions, number of examples, metric implementation, and failed-example analysis. Publish prediction files where licensing permits, and clearly distinguish official scores from your own extensions.

For a multi-language study, the same discipline used in benchmarking NLP models for Telugu and Sanskrit helps prevent language-specific results from being buried in a single average. If you are building a production evaluation harness, also consider a separate section on cost, latency, and robustness instead of mixing those measures with linguistic quality.

Common mistakes to avoid

• Treating an unverified model card or dataset mirror as authoritative.
• Reporting a score without the prompt and decoding configuration.
• Applying English-centric tokenisation or text cleaning to Nepali.
• Comparing different dataset splits or silently dropping failed examples.
• Using one automated metric as a substitute for bilingual review.
• Fine-tuning on data that overlaps with the benchmark.
• Updating a model or dataset without preserving the prior revision.

A practical 2026 checklist

Before publishing your Nepali IndicGenBench result, confirm that you have:

• frozen model and dataset revisions;
• validated Unicode and tokenizer behaviour;
• used the official Nepali split and evaluator;
• saved raw predictions and run metadata;
• fixed generation settings across models;
• reported per-task results and meaningful error slices;
• separated automated scores from human judgements;
• documented compute, latency, and licensing constraints.

The value of Hugging Face here is not simply convenient model download. Combined with a controlled IndicGenBench run, it gives Indian AI teams a repeatable way to find where Nepali systems work, where they fail, and whether an apparent improvement survives careful evaluation.