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Short answer: NVIDIA’s Nemotron-pretraining-Indic-v1 corpus is not yet publicly downloadable. NVIDIA and AI4Bharat announced it in 2026 as a forthcoming corpus of roughly 2.8 trillion tokens, so there is currently no authoritative repository URL, checksum manifest, license text, artifact list, or final record schema to use. Do not trust guessed paths or third-party mirrors as the official release.
You can still prepare a correct, auditable workflow now. Once NVIDIA or AI4Bharat publishes the official dataset card and repository, download the selected artifact, pin its revision, verify the release checksum, validate the file, preserve provenance, split by source family, clean and deduplicate the text, retain quality metadata, and export it in the format your training stack expects.
What Nemotron-pretraining-Indic-v1 contains
The announced corpus is designed for multilingual pretraining across the 22 languages listed in India’s Constitution. Native-script material covers all 22 languages. Twelve of those languages also have romanized variants, producing 34 language-and-script configurations in total. The announcement describes an open, GPU-accelerated generation pipeline rather than merely a static collection of files.
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|---|---|
| Roughly 2.8 trillion tokens | A scale estimate from the 2026 NVIDIA and AI4Bharat announcement, not a final downloadable-file count. |
| 22 constitutional languages | Native-script content is intended for every language in the stated scope. |
| 34 language-and-script configurations | Twelve languages add romanized forms to their native-script material. |
| Open generation pipeline | The release is associated with reproducible generation and curation processes, not only prebuilt data files. |
| Forthcoming release | The authoritative repository, license, checksums, artifact names, and schema have not been published in the announcement. |
Those missing release details are operationally important. A dataset cannot be verified from its name alone: you need the exact repository revision, file name, digest algorithm, digest value, license terms, and schema supplied by the publisher.
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Is the corpus available to download yet?
Not according to the 2026 announcement. Treat the corpus as forthcoming until an NVIDIA or AI4Bharat dataset card or release repository identifies the official files. A page that uses a similar name, a copied announcement, or a mirror with no publisher-controlled revision is not sufficient evidence that you have the released corpus.
Do not fill the gap with a guessed Hugging Face path, an unverified object-storage URL, or a checksum copied from another NVIDIA model. NVIDIA’s multilingual NeMo tutorial publishes an md5sum example for a model artifact; that hash does not belong to Nemotron-pretraining-Indic-v1 and must never be reused for it.
What must be published before you run a production download
- An official repository or release page controlled by NVIDIA or AI4Bharat.
- The exact artifact names and available splits, such as
train, validation, or test. - A license and any access conditions, including whether the repository is gated.
- A final record schema describing text, language, script, provenance, quality, and repair fields.
- A checksum manifest with the algorithm and digest for every downloadable artifact.
- A revision identifier that can be recorded and reproduced later.
Download the corpus when the official repository exists
NVIDIA’s NeMo Gym documentation shows the supported Hugging Face download pattern. You either select a structured dataset split or identify one raw artifact file. The two modes are alternatives: do not provide split and artifact_fpath in the same command.
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gym dataset download
--repo-id <official-org>/<official-dataset>
--split train
--output ./data/indic/train.jsonlReplace the angle-bracketed values only after checking the official dataset card. The example assumes that the publisher exposes a structured train split and that the card specifies JSONL output. If the release uses another format or split name, follow that schema instead of forcing the example to fit.
Download a named raw artifact
gym dataset download
--repo-id <official-org>/<official-dataset>
--artifact-fpath <exact-file-name>
--output ./data/indic/<exact-file-name>Raw-file mode copies the exact artifact named by the release. It is appropriate for a tarball, Parquet file, compressed JSONL file, or another publisher-defined object when you are not asking the tool to resolve a dataset split.
Authentication, cache, and reproducibility
Gated repositories require a valid access token and acceptance of the repository license. Authenticate through the supported client before starting a large transfer, and save evidence of the license-acceptance state in your run manifest. The documented tool uses the Hugging Face cache; make the destination explicit so that a cached object is not mistaken for a newly verified release.
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Record these values immediately after downloading:
- Repository identifier and immutable revision or commit.
- Selected split or exact artifact path.
- Download timestamp in UTC.
- Local output path and file size.
- Account or access context used for a gated release, without storing a secret token.
- License version or acceptance record shown by the publisher.
Verify integrity before transforming any file
Verification is a release-integrity check, not a quality assessment. A matching digest proves that your local bytes match the publisher’s file; it does not prove that the text is clean, balanced across languages, or safe from train–test leakage.
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- Obtain the manifest from the same authenticated release channel. Download the checksum manifest published with the data, not a digest pasted into an unrelated forum or mirror.
- Identify the named algorithm. Use exactly the algorithm stated by the release, such as SHA-256 or MD5. Do not silently substitute another algorithm or compare a truncated digest.
- Compute the local digest. For a SHA-256 manifest, run
sha256sum path/to/file; for an MD5 manifest, runmd5sum path/to/file. Compare the complete hexadecimal value and the file name. - Reject mismatches. Delete or quarantine the failed download, inspect the revision and path, then redownload. Do not “accept” a mismatch because the file opens or has approximately the expected size.
- Record the verified result. Store the algorithm, digest, byte size, revision, timestamp, and manifest identity in your dataset manifest.
- Validate the container. Decompress archives, open Parquet or other columnar files with a reader, parse JSONL line by line, and count records before making transformations.
A checksum mismatch can come from a partial transfer, a changed repository revision, a proxy cache, or an incorrectly selected artifact. Preserve the failed digest and error details if your pipeline needs an audit trail.
Preserve the release’s provenance and schema
The announcement says the generation pipeline retains source identifiers while producing translations, repaired records, and Latin-script variants. That identifier is the key to both auditability and leakage-safe evaluation. Keep the original source relationship even after text cleaning or conversion.
| Field or concept | How to handle it |
|---|---|
| Source identifier | Keep the publisher’s identifier unchanged; use it to group related records and assign splits. |
| Text | Retain the released text and, where possible, a separate transformed version rather than overwriting the original. |
| Language | Store the declared language exactly as released and validate it against the content. |
| Script | Distinguish native script from Latin or other script variants; do not infer script solely from a language label. |
| Variant type | Record whether a row is original, translated, repaired, romanized, or another publisher-defined variant. |
| Source family | Keep a grouping key for all records derived from the same underlying document, even if their text differs. |
| Quality and repair metadata | Preserve assessment scores, repair status, and filtering decisions for later auditing. |
The names text and source_id are useful validation examples, not confirmed field names for the unreleased corpus. Adapt your parser only after reading the official schema.
Split data by source identifier to prevent leakage
Random row-level splitting is unsafe for this corpus. A single English source can produce a translation, a repaired translation, and a romanized version. If those rows are randomly distributed, the model may see near-duplicates of an evaluation example during training and produce an inflated score.
- Load all records while retaining the publisher’s source identifier and variant metadata.
- Build a group for every source family. Include all translations, repairs, script variants, and other derivatives of the same source in that group.
- Assign complete groups—not individual rows—to train, validation, and test partitions.
- Check that no source identifier appears in more than one partition.
- Report partition sizes by language, script, source family, and variant type.
- Run exact and fuzzy overlap checks after splitting, because two different identifiers can still contain copied or syndicated text.
For multilingual evaluation, stratify the assignment so each language-and-script configuration is represented where the corpus and your task permit it. Do not let a high-volume language dominate every partition while a low-volume configuration appears only in training.
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Normalize and validate multilingual text
Indic scripts and romanized text can be damaged by inconsistent Unicode handling, invisible controls, or incorrect language labels. Normalize before deduplication and tokenization, but keep the pre-normalized text and transformation record when storage allows it.
Recommended cleaning sequence
- Decode the file as declared by the release and fail loudly on undecodable bytes.
- Apply the Unicode normalization form appropriate to your downstream policy. Use one documented form consistently across all splits.
- Remove malformed control characters while preserving legitimate whitespace and punctuation.
- Normalize line endings and collapse accidental formatting artifacts without deleting meaningful script characters.
- Validate that the declared language and script are plausible for the content. Route uncertain rows to quarantine instead of silently relabeling them.
- Store a transformation status, software version, and timestamp for every batch.
Do not transliterate native-script text merely to simplify a pipeline. Native and romanized configurations are distinct data products; preserving both is necessary for the comparisons the release describes.
Deduplicate documents and source families
Use more than one deduplication method. Byte-identical files are easy to find, but translations and romanizations can be semantically duplicated while differing in every character.
Exact deduplication
Compute a stable document hash after applying the documented normalization policy. Keep one canonical record or mark duplicates according to the release’s provenance rules. Store the duplicate relationship rather than deleting evidence without a record.
Fuzzy deduplication
NVIDIA NeMo Curator documents MinHash and locality-sensitive hashing (LSH) for generating candidate near-duplicate pairs, followed by similarity checks for the candidates. Tune thresholds on representative samples from each language and script; a threshold that works for English text may behave differently on short Indic documents or romanized variants.
Semantic and provenance grouping
Text deduplication is not enough. Group records by source family before splitting because a translation and its romanization may be semantically identical without triggering a byte-level or character-level collision. Keep the source identifier and variant type attached to every deduplication decision.
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Apply quality filters without losing audit fields
The announced pipeline uses an automated assessment acceptance threshold of at least 75 out of 100. This is an inclusion threshold for an automated score, not a claim that the accepted text is 75 percent accurate. Store the score and the rule that produced the keep or reject decision.
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The announcement also reports 438.4 billion tokens recovered through repair. Repaired material should remain distinguishable from untouched material so users can measure how much of a training run depends on corrective processing.
- Retain the original record, repaired text, repair status, and repair reason where available.
- Store the automated quality score and threshold version.
- Record language, script, source identifier, source family, and variant type after every transformation.
- Quarantine records that fail parsing, language/script validation, or safety policies instead of silently dropping them.
- Keep counts and token estimates before and after each filter so the final corpus size is explainable.
Export JSONL, Parquet, or another training format
JSONL requirements
If the official release specifies JSONL, write one valid JSON object per line. Escape embedded newlines inside string values, use UTF-8, and avoid adding comments or a header row. Keep metadata fields in the object rather than placing them in an undocumented side file.
import json
from pathlib import Path
src = Path('train.jsonl')
with src.open(encoding='utf-8') as f:
for line_no, line in enumerate(f, 1):
record = json.loads(line)
assert isinstance(record, dict), line_no
assert record.get('text'), line_no
assert record.get('source_id'), line_noThis validator demonstrates the checks you should adapt to the official schema. It does not establish that the released fields will literally be named text and source_id.
Columnar formats
If the publisher supplies Parquet or another columnar format, preserve the declared schema, logical types, compression settings where relevant, and metadata. Do not flatten nested provenance or quality structures just to make a quick text file. If your trainer needs JSONL, create a documented derivative and retain the verified original artifact.
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Record counts and token accounting
Count records before and after conversion. Calculate token estimates with the tokenizer used by your training run, and report counts by language, script, source family, and variant type. A headline total of roughly 2.8 trillion tokens does not guarantee that every derivative export will contain the same number after filtering, deduplication, or sampling.
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Prepare a tokenizer corpus without erasing language balance
NVIDIA’s multilingual NeMo tutorial demonstrates sampling prepared JSONL into a plain-text corpus, training a tokenizer from an existing tokenizer or an iterator, and discussing vocabulary and merge-file combination. For this Indic release, sampling only by raw token volume would overrepresent the largest configurations and underrepresent smaller ones.
- Define a sampling policy before creating the tokenizer corpus.
- Set minimum representation targets for every available language-and-script configuration.
- Sample by source family so related variants do not dominate the sample.
- Keep native and romanized text distinguishable in your accounting, even if they share a tokenizer.
- Train or extend the tokenizer on the sampled text, then inspect token lengths and unknown-token behavior separately for each configuration.
- Record the source revision, sampling seed, sampling counts, tokenizer base, vocabulary size, and merge files.
Tokenizer quality should be evaluated per language and script, not only by a single aggregate compression number. A vocabulary that performs well on high-volume native-script data can still fragment romanized or lower-volume configurations excessively.
Compare release artifacts if several are offered
When the official repository publishes multiple variants, compare them on the dimensions below rather than choosing solely by file size.
| Comparison axis | Questions to answer |
|---|---|
| Script coverage | Does the artifact contain native scripts, romanized forms, or both? |
| Provenance | Are source identifiers and source-family relationships complete? |
| Quality metadata | Are assessment scores, repair status, and filtering decisions included? |
| Format and streaming | Is the file JSONL, Parquet, or another format, and can your loader stream it? |
| Integrity controls | Are checksums, revisions, and an authenticated manifest supplied? |
| License and redistribution | Can you use, modify, and redistribute the artifact for your intended purpose? |
| Language balance | How many tokens and source families are present in each configuration? |
If a value is not stated in the release documentation, mark it as “not stated” in your comparison rather than inferring it from a filename.
Keep an auditable dataset manifest
Create one machine-readable manifest for every downloaded and transformed artifact. At minimum, include:
- Publisher, repository identifier, and immutable revision.
- Artifact or split name and original file name.
- License name, version, and gated-access acceptance status.
- Checksum algorithm, published digest, locally computed digest, and verification timestamp.
- File size, compressed and uncompressed where applicable, and record count.
- Language, script, source-family, and variant-count summaries.
- Cleaning, deduplication, repair, and quality-filter versions and parameters.
- Train, validation, and test assignment policy, including the grouping key.
- Tokenizer name or revision, sampling seed, and generated derivative paths.
This manifest lets another engineer reproduce your exact input and distinguish a publisher change from a local processing change.
Troubleshoot common failures
| Symptom | Likely cause | Action |
|---|---|---|
| Repository not found | The official release is still forthcoming, or the identifier is guessed. | Stop and wait for the publisher’s dataset card; do not substitute a mirror. |
| Access denied | The repository is gated or the license has not been accepted. | Authenticate with a valid token and complete the publisher’s acceptance flow. |
| Checksum mismatch | Partial transfer, wrong revision, wrong artifact, or stale cache. | Compare the manifest path and revision, clear the failed artifact, and redownload. |
| JSON parsing error | Truncated file, wrong decompression, or a non-JSONL artifact. | Verify the container type, decompress correctly, and rerun line-level validation. |
| Unexpected duplicate evaluation text | Rows were split randomly instead of by source family. | Rebuild partitions from source identifiers and rerun overlap checks. |
| One language dominates tokenizer training | Sampling followed raw token volume. | Use configuration-aware sampling and publish per-language counts. |
| Script validation failures | Incorrect labels, mixed scripts, or damaged Unicode. | Quarantine affected rows, inspect normalization, and preserve the original record. |
Practical release-day checklist
- Confirm that the repository is linked from an NVIDIA or AI4Bharat release page.
- Read the license, schema, artifact list, and revision policy.
- Accept any gate and authenticate without exposing credentials in logs.
- Download one selected split or raw artifact using the documented command.
- Verify its publisher-provided checksum before opening or transforming it.
- Validate decompression, encoding, record structure, and counts.
- Preserve source identifiers, language, script, variant, quality, and repair metadata.
- Normalize Unicode and remove malformed controls while keeping transformation records.
- Deduplicate exactly and fuzzily, then group related source families.
- Assign complete source groups to train, validation, and test partitions.
- Export a documented JSONL or columnar derivative only after the verified original is archived.
- Build tokenizer samples with explicit language-and-script balancing and record the seed.
Until the official repository, license, checksums, artifacts, and schema are published, the correct action is preparation rather than an invented download command. Once those release facts exist, this workflow gives you a verifiable, leakage-resistant, and format-preserving path from the publisher’s files to a training-ready Indic corpus.
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