You cannot download Nemotron-pretraining-Indic-v1 yet. NVIDIA and AI4Bharat announced the roughly 2.8-trillion-token corpus as forthcoming, but the announcement does not provide an authoritative repository, license, artifact list, checksum manifest, or final record schema. Do not use a guessed URL or an unverified mirror. This guide shows how to prepare a reproducible workflow now and exactly how to download, verify, clean, split, and export the corpus once NVIDIA or AI4Bharat publishes the official release.
Is NVIDIA’s Indic corpus available yet?
As of the 2026 announcement, Nemotron-pretraining-Indic-v1 is forthcoming rather than publicly downloadable. There is no confirmed repository address, release filename, checksum, license text, or final schema that can support an honest copy-and-paste download command today. A third-party mirror should be treated as unverified until the official dataset card or release repository identifies it.
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This distinction matters operationally. A training run that starts from an unofficial copy may use different files, altered records, a different revision, or terms that do not permit redistribution. Record the announcement as a planned release, not as evidence that a file is already available.
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What the announced corpus contains
The headline size is approximately 2.8 trillion tokens. NVIDIA and AI4Bharat describe coverage of all 22 languages listed in India’s Constitution. Every language has native-script material; 12 languages additionally have romanized variants. That produces 34 language-and-script configurations in total.
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| Attribute | Announced value | Qualification |
|---|---|---|
| Corpus size | Roughly 2.8 trillion tokens | Figure reported by NVIDIA and AI4Bharat in their 2026 announcement; final release counts may differ. |
| Constitutional-language coverage | 22 languages | Native-script material is described for all 22. |
| Romanized coverage | 12 languages | These languages have both native and Latin-script variants. |
| Language/script configurations | 34 | Combined native and romanized configurations announced for the corpus. |
| Automated quality gate | At least 75 out of 100 | An acceptance threshold, not a claim that records are 75 percent accurate. |
| Tokens recovered through repair | 438.4 billion | Announcement figure; retain repair metadata so users can distinguish repaired material. |
The release is presented as an open, GPU-accelerated generation pipeline as well as a corpus. That means users should expect provenance and transformation information to be important parts of the usable release, not optional documentation.
Prepare a reproducible workspace before downloading
Create a directory layout that separates immutable source artifacts from transformed training files. For example:
data/indic/raw/data/indic/verified/data/indic/processed/data/indic/manifests/Keep a local manifest outside the corpus files. At minimum, reserve fields for:
- the exact repository identifier and revision or commit;
- the selected split or raw artifact name;
- download timestamp in UTC;
- license-acceptance state and the account or token context used;
- file size and release-provided digest;
- the digest algorithm, such as SHA-256 or MD5;
- record and token counts before transformation;
- software versions used for validation, normalization, deduplication, and export.
Do not call these fields the corpus schema. They are your audit manifest, and they remain useful even if the final records use different field names.
Download from the official repository when it is published
NVIDIA’s NeMo Gym documentation shows the supported Hugging Face download pattern. It accepts either a structured dataset split or one raw artifact. Use one mode at a time: a command that specifies --split must not also specify --artifact-fpath.
Structured split download
After checking the official dataset card for the real organization, repository name, split, and license, adapt this pattern:
gym dataset download
--repo-id <official-org>/<official-dataset>
--split train
--output ./data/indic/train.jsonlThe documented workflow converts a structured dataset to JSONL at the requested output path. It uses the Hugging Face cache, so the output file and the cache revision should both be accounted for in your manifest.
Raw artifact download
If the release lists a named file rather than a structured split, use the exact artifact filename shown by the official release:
gym dataset download
--repo-id <official-org>/<official-dataset>
--artifact-fpath <exact-file-name>
--output ./data/indic/<exact-file-name>Do not infer an extension, rename a shard before verification, or substitute a similarly named file. Artifact names often encode whether a file is native-script data, romanized data, metadata, or a particular split.
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Gated repositories and authentication
If the repository is gated, accept its license in the hosting service and provide a valid access token through the documented authentication method. Save the fact that acceptance occurred, but never put a token in a shell transcript, manifest committed to source control, or article command. A successful HTTP response is not proof that you received the intended revision; record the revision identifier returned by the release tooling.
Pin the release before processing
Use a tag, commit, or other immutable revision if the official repository supports one. Downloading “latest” twice can produce different training data. Store the revision beside the digest, output path, and timestamp. If the release supplies multiple language-balanced shards, record every shard rather than only the aggregate directory.
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Verification has four separate jobs: authenticate the release source, compare the bytes with the published digest, prove that the file can be decoded, and count the records you intend to process.
1. Obtain the release checksum manifest
Download the checksum manifest through the same authenticated channel as the data, or from the exact official release page identified by the dataset card. The manifest must name the algorithm and the corresponding artifact. If no official digest is published, mark the file unverified instead of inventing a checksum from a mirror.
2. Compare the local digest
Run the utility that matches the release’s stated algorithm. For example, a SHA-256 manifest can be checked with:
sha256sum --check checksums.sha256An MD5 manifest can be checked with:
md5sum --check checksums.md5Use only the algorithm named by the release. NVIDIA’s multilingual NeMo tutorial demonstrates an MD5 workflow for an example model artifact; that published model hash is not a Nemotron-pretraining-Indic-v1 checksum and must never be reused for this corpus.
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A mismatch means the local bytes are not the bytes described by the release. Delete or quarantine the file, redownload the same revision, and check storage or proxy issues if the second attempt fails. Do not “fix” a mismatch by editing the manifest or continuing to preprocessing.
4. Validate readability and counts
After the digest passes, test decompression, Parquet decoding, or JSONL parsing as appropriate. Count records before filtering and retain the count in your manifest. A valid checksum proves byte identity, not that your parser handled every row or that a compressed archive contains the expected files.
Inspect the final schema instead of assuming it
The announcement does not publish a final record schema. A local validator can require fields only after the official dataset card confirms their names. The following example uses text and source_id as validation placeholders, not as claims about the unreleased records:
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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_noBefore running a production job, inspect several records from every language/script configuration and document the actual field mapping. Keep unknown fields during the first pass; they may contain source-family, quality, repair, or variant information needed later for leakage-safe splitting.
Preserve provenance as the primary formatting rule
The generation pipeline is described as retaining source identifiers while producing translations, repaired text, and Latin-script variants. Treat the source identifier as a lineage key. Keep the original English-document relationship, translated records, repairs, and script variants connected even when their text differs completely at the byte level.
At minimum, carry these metadata categories when the release provides them:
- Source identity: source identifier, source family, and any document-level lineage key.
- Language: declared language code and the language used for quality checks.
- Script: native script or Latin/romanized representation.
- Variant type: original, translated, repaired, transliterated, or other release-defined status.
- Quality: automated score, acceptance decision, and assessment version if supplied.
- Repair: whether a repair was applied and, if available, what operation produced it.
Do not flatten these fields into a single prose prefix. Separate columns or JSON keys allow you to audit a language, remove a variant, or reproduce a split without rewriting the text.
Normalize Unicode and validate language/script claims
Indic text can contain combining marks, multiple equivalent Unicode sequences, punctuation variants, and malformed control characters. Choose a Unicode normalization form appropriate to the scripts represented by the release, and apply it consistently to every text field used for training. Remove malformed control characters that cannot serve a documented purpose, but preserve the original text and transformation metadata when storage permits.
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Validate that declared language and script agree with the content. Route suspicious records to a quarantine set for review instead of silently relabeling them. A romanized record should not be counted as native-script coverage merely because its language field names the same language.
Deduplicate documents and variants without losing lineage
Exact duplicates
Compute a stable document hash after the release-defined normalization policy and remove or quarantine byte-identical documents according to your training policy. NVIDIA NeMo Curator documents exact deduplication by document hash. Keep the winning record’s provenance and a count of removed copies.
Near duplicates
Use MinHash/LSH or an equivalent candidate-generation method for fuzzy deduplication, then perform similarity checks on each candidate collision. MinHash is a way to find likely overlaps; it is not a license to delete every similar paragraph. Preserve source-family information so a legitimate translation or a short common phrase is not confused with accidental duplication.
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Semantic duplicates across scripts
Native and romanized variants can represent the same content without sharing bytes, and translations can be semantically equivalent in different languages. Text-only deduplication will miss these relationships. The source identifier and source-family key must therefore participate in deduplication and split assignment.
Split by source identifier to prevent evaluation leakage
Never make a random row split when translated, repaired, and romanized records share an origin. A random split can place one language variant in training and its near-identical counterpart in evaluation, inflating scores.
- Construct a grouping key from the source identifier or the strongest source-family field supplied by the release.
- Check that every related translation, repair, and script variant maps to the same group.
- Assign groups—not individual rows—to train, validation, and test partitions.
- After assignment, verify that no source identifier occurs in more than one partition.
- Report partition counts by language, script, variant type, and quality band.
If a record lacks a source identifier, place it in a separately tracked pool and define a conservative policy before training. Do not silently mix unidentified records into a source-grouped evaluation set.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Apply the quality threshold with an audit trail
The announcement describes an automated-assessment acceptance threshold of at least 75 out of 100. This is a gate for inclusion, not a statement that accepted text is 75 percent accurate. Store the numeric score, the threshold used, and the assessment version where available.
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The announcement also reports 438.4 billion tokens recovered through repair. Repaired material should remain distinguishable from untouched material. Keep repair status and source lineage so you can evaluate whether repairs improve or harm performance for a particular language or script. If you publish a derivative, document whether repaired and unrepaired records were both retained.
- Do not replace a missing score with zero; mark it as missing.
- Do not recompute the threshold after filtering without recording the new rule.
- Do not describe the threshold as a precision, recall, or human-accuracy percentage.
- Keep rejected-record counts by language and variant so coverage losses are visible.
Export JSONL or preserve a columnar schema
JSONL export
JSONL means one complete JSON object per line. Escape embedded newlines inside string values, emit UTF-8, and fail the export if any line is not independently parseable. Keep metadata in the same object unless the official training stack explicitly requires a sidecar file.
Use a temporary output followed by an atomic rename, and record the number of written lines. That prevents an interrupted job from being mistaken for a complete shard.
Parquet or another columnar format
If the release uses Parquet or another columnar format, preserve the declared schema, logical types, compression settings, and metadata. Flattening nested provenance fields into text can make later source grouping impossible. Export a JSONL derivative only after you have retained the original verified files.
Sampling a tokenizer corpus
NVIDIA’s multilingual NeMo workflow shows how to sample prepared JSONL text into a plain-text corpus for tokenizer training. For this release, sample by language-and-script configuration rather than by raw token volume alone. Otherwise, the largest configurations can dominate the vocabulary and leave smaller scripts underrepresented.
Prepare a tokenizer without erasing script balance
Decide whether one multilingual tokenizer or several language-specific tokenizers fit your model before sampling. For a multilingual tokenizer, set explicit quotas or weights for all 34 configurations, document whether quotas are token-based or document-based, and retain a seed for random sampling.
Train from an existing tokenizer or an iterator as supported by the NeMo workflow, and record vocabulary size, normalization rules, special tokens, merge files, and the exact sample manifest. If you merge vocabularies or merge files, keep the source versions and collision policy. Evaluate tokenization separately for native and romanized forms; a low average token count can hide severe fragmentation in one script.
Compare release variants when more than one artifact appears
If the official release offers multiple downloads, compare them using the same fields rather than choosing solely by file size:
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| Decision axis | Questions to answer |
|---|---|
| Script coverage | Does the artifact contain native scripts, romanized variants, or both? |
| Provenance | Are source identifiers and source-family relationships complete? |
| Quality metadata | Are scores, repair flags, and assessment versions retained? |
| Format and streaming | Can your loader stream the files, and does it preserve nested metadata? |
| Integrity controls | Are checksums and immutable revisions published? |
| Legal terms | Does the license permit your intended training, modification, and redistribution? |
| Balance | Are token counts reported per language and script rather than only as one total? |
Mark any unanswered cell as “not stated” in your comparison notes. Do not infer a license, checksum, or language balance from a filename.
Troubleshoot common failures
There is no official repository
Stop at preparation. Do not substitute a guessed organization name, a search result, or a mirror. Build the manifest structure and processing scripts, then fill in the repository identifier only after the official release publishes it.
The download command reports an authorization error
Confirm that the account accepted the repository license, that the token has read access, and that the repository is the one named by the official dataset card. Do not paste the token into the command history. Retry after authentication is corrected, then verify the resulting file digest.
The tool rejects the command syntax
Check whether you supplied both --split and --artifact-fpath; the documented modes are mutually exclusive. Also confirm that the output directory exists and that the split or artifact name exactly matches the release.
The checksum fails repeatedly
Compare the local file size with the release manifest, clear a corrupt cache entry, and redownload the pinned revision. If the mismatch persists, quarantine the artifact and contact the release maintainers rather than editing the checksum file.
JSONL parsing stops partway through a shard
Identify the first failing line, verify that decompression completed, and compare the shard size and digest again. A truncated download or an unescaped embedded newline is more likely than a harmless malformed record. Keep the original verified artifact and regenerate the derivative after fixing the parser or exporter.
Evaluation scores look implausibly high
Audit source identifiers across partitions, including translated and romanized variants. A row-level random split is the usual cause of leakage. Rebuild partitions by source group and report the language/script composition of each set.
Minimum publication checklist
- Official repository and immutable revision recorded.
- License acceptance and redistribution terms documented.
- Every artifact matched to an official checksum and algorithm.
- Compressed files, Parquet files, or JSONL shards passed readability checks.
- Record and token counts captured before transformation.
- Actual schema inspected and field mapping documented.
- Unicode policy, control-character handling, and language/script validation recorded.
- Exact and fuzzy deduplication settings reproducible.
- Source-grouped train, validation, and test assignments verified for disjointness.
- Quality scores, 75/100 acceptance decisions, repair flags, and provenance retained.
- Tokenizer sampling covers all relevant language/script configurations.
- Output files, software versions, seeds, and manifests archived with the run.
The practical rule is simple: wait for the authoritative release, pin exactly what you downloaded, verify bytes before parsing, and preserve lineage through every transformation. That is what makes a 2.8-trillion-token multilingual corpus usable rather than merely large.
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