1GPU LLM
Collection
Language models trained from scratch on a single consumer GPU. • 5 items • Updated
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
name: string
level: string
natural_docs: int64
code_docs: int64
provenance: struct<selection: string, vs_train_gates: string, internal_scan: string, swaps: struct<stack_edu_pyt (... 140 chars omitted)
child 0, selection: string
child 1, vs_train_gates: string
child 2, internal_scan: string
child 3, swaps: struct<stack_edu_python: list<item: string>, stack_edu_shell: list<item: string>, stack_edu_typescri (... 21 chars omitted)
child 0, stack_edu_python: list<item: string>
child 0, item: string
child 1, stack_edu_shell: list<item: string>
child 0, item: string
child 2, stack_edu_typescript: list<item: null>
child 0, item: null
child 4, typescript_shortfall_accepted: int64
gates: struct<VALIDATION_TRAIN_EXACT_OVERLAP: int64, VALIDATION_TRAIN_NEAR_OVERLAP: int64, VALIDATION_INTER (... 58 chars omitted)
child 0, VALIDATION_TRAIN_EXACT_OVERLAP: int64
child 1, VALIDATION_TRAIN_NEAR_OVERLAP: int64
child 2, VALIDATION_INTERNAL_EXACT_DUP: int64
child 3, VALIDATION_INTERNAL_NEAR_DUP: int64
text: string
doc_id: string
to
{'doc_id': Value('string'), 'text': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
name: string
level: string
natural_docs: int64
code_docs: int64
provenance: struct<selection: string, vs_train_gates: string, internal_scan: string, swaps: struct<stack_edu_pyt (... 140 chars omitted)
child 0, selection: string
child 1, vs_train_gates: string
child 2, internal_scan: string
child 3, swaps: struct<stack_edu_python: list<item: string>, stack_edu_shell: list<item: string>, stack_edu_typescri (... 21 chars omitted)
child 0, stack_edu_python: list<item: string>
child 0, item: string
child 1, stack_edu_shell: list<item: string>
child 0, item: string
child 2, stack_edu_typescript: list<item: null>
child 0, item: null
child 4, typescript_shortfall_accepted: int64
gates: struct<VALIDATION_TRAIN_EXACT_OVERLAP: int64, VALIDATION_TRAIN_NEAR_OVERLAP: int64, VALIDATION_INTER (... 58 chars omitted)
child 0, VALIDATION_TRAIN_EXACT_OVERLAP: int64
child 1, VALIDATION_TRAIN_NEAR_OVERLAP: int64
child 2, VALIDATION_INTERNAL_EXACT_DUP: int64
child 3, VALIDATION_INTERNAL_NEAR_DUP: int64
text: string
doc_id: string
to
{'doc_id': Value('string'), 'text': Value('string')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1880, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
doc_id string | text string |
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}
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0014980de2c88b1e148712941f174df1 | {
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00158efd8c15fc9ef3a287f95685a137 | {
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],
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true,
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001637b33b294c5855cb8aebd2588d4c | {
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],
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0018f586bbe17e2587e57543db3add2e | {
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},
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},
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],
"author": "Travis Collins <travis@tec27.com> (http://tec27.c... |
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002e57e4c2513637e029a20026687723 | {
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0033990c8cd48b367c1e2d45ca8cd147 | {
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0034a41b07a053134f915fb8eadb22fc | {
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{
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003ee561032c2f05d3399c84a4c10066 | {
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003f2ecdf2494e60914abec7368db083 | {
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{
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004cdedb5850929cdfeed50f2c828bb6 | {
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0050b055249a8fb8dbca97bea74d677a | {
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},
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005e98a5c69897b674eded33c7ed5454 | {
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}
}
}
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"build": "./node_modules/react-tools/bin/jsx index.jsx > index.js",
"prepublish": "npm run build"
... |
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"description": "Azure Application Insights transport for Winston",
"main": "./lib/winston-azure-application-insights.js",
"directories": {
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},
"scripts": {
"test": "mocha"
},
"repository": {
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... |
00674b545ef299c360b82c1f06b8e869 | {
"name": "<%= projectName %>",
"version": "0.0.0",
"description": "<%= projectDescription %>",
"license": "MIT",
"repository": "<%= githubUsername %>/<%= projectName %>",
"scripts": {
"start": "npm run dev:server",
"deploy": "surge dist || exit 0",
"dev:server": "scripts/dev-server.sh",
"bu... |
0069e4eadd3f75caf7ab3bc55239b25b | {
"test_framework" : "qunit",
"test_path": ["tests/index.html", "tests/functional.html"],
"browsers": [
"chrome_40",
"chrome_latest",
"firefox_36",
"firefox_latest",
"safari_7_1",
"safari_latest",
"ie_9",
"ie_10",
"ie_11",
"ie_latest",
"edge_latest",
"opera_12_16",
"opera_30",
"opera_late... |
006ae5d13d42b678dcb60392fcccc8e2 | {
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00f3c38ab1ff0ecd856590df544cb0bc | {
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00f539721af63ce368ba66a63a9c6390 | {
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End of preview.
1GPU LLM Validation Corpus (EN/IT/Code, document-level)
Validation-only corpus for the 1GPU LLM pretraining project (nazdef/1gpu-llm Collection).
This repository contains the frozen document-level validation master (T050-SUB002, COMPLETE 2026-09-24), not the packed 48k/ctx2500 training artifact. The packed validation set (~10M tokens) is derived downstream from this master and stays local.
Relationship to the training corpus
- Training corpus: nazdef/1gpu-llm-pretraining-corpus-15b-en-it-code, canonical data revision
333c4001551757aedb3454f30c8a6c08eaf23e12. - Every document in this validation set is drawn from residual pools excluded from the frozen 15B training selection (Registry manifest
3fa8a8ba31f8f0e3, 14.933.001.570 tokens / 18.375.507 docs). - Do not train on this corpus if you evaluate against it.
Contents
24.788 documents, 15 files:
| File | Docs | Source |
|---|---|---|
natural/0000-….jsonl |
9.969 | EN FineWeb-HQ |
natural/0001-….jsonl |
1.115 | EN Wiki40B |
natural/0002-….jsonl |
11.226 | IT FineWeb2-HQ |
natural/0003-….jsonl |
575 | IT Wiki40B |
code/commitpackft_json.jsonl |
151 | commitpackft json |
code/commitpackft_toml.jsonl |
14 | commitpackft toml |
code/commitpackft_yaml.jsonl |
370 | commitpackft yaml |
code/github_code_clean_css.jsonl |
6 | github_code_clean css |
code/github_code_clean_html.jsonl |
14 | github_code_clean html |
code/stack_edu_javascript.jsonl |
213 | stack_edu javascript |
code/stack_edu_python.jsonl |
719 | stack_edu python |
code/stack_edu_shell.jsonl |
182 | stack_edu shell |
code/stack_edu_sql.jsonl |
169 | stack_edu sql |
code/stack_edu_typescript.jsonl |
65 | stack_edu typescript |
manifest.json + SHA256SUMS |
— | provenance + checksums |
Totals: EN 11.084 / IT 11.801 / CODE 1.903. Estimated ~18,0M natural + ~1,84M CODE census tokens (includes ~2x headroom; the packed 10M set — EN 4.537.500 / IT 4.482.500 / CODE 980.000 — is selected downstream preserving this mix).
Each line: {"record_index": int, "text": str} (natural) or {"doc_id": str, "text": str} (CODE). Natural files are in census-rank (best-first) order.
Selection (deterministic)
- Residual census of everything not selected for training (SUB001, overlap 0).
- Rank-ASC walk skipping in-training exact hashes (v2 repick; loser-bias fix documented in repo history).
- Contamination gates vs training (below); excluded replaced deterministically from same pool/source.
- Master subset: rank-ASC / doc_id order up to ~2x packed-token need per source, preserving the SUB001 source mix (EN web 90,2% / wiki 9,8%; IT web 96,7% / wiki 3,3%; CODE 10 sub-buckets per training mix).
- Rerun audit byte-identical (PASS).
Contamination gates (final master, all zero)
VALIDATION_TRAIN_EXACT_OVERLAP = 0(sha256 of normalized text vs training manifest)VALIDATION_TRAIN_NEAR_OVERLAP = 0(64-bit project fingerprints, Hamming ≤ 6 vs training)VALIDATION_INTERNAL_EXACT_DUP = 0VALIDATION_INTERNAL_NEAR_DUP = 0(per family: census sigs natural, text_signature CODE)
1.128 natural + CODE candidates excluded vs training; 11 internal CODE dupes found (4 swapped from certified survivors).
Known limitation — TypeScript shortfall
7 TypeScript internal-dupes could not be replaced: the residual pool (276 further candidates) contains only near-duplicates under the frozen Hamming ≤ 6 semantics. Accepted as shortfall: CODE total stays 1,88x need, TypeScript at 96% of its mix share (0,5pp mix deviation, recorded for the packing step).
Normalization
Natural: project _normalized_document (same as training pipeline). CODE: exact byte content, strip of 13 control chars + NFC, FEFF preserved (same as training release).
Provenance / licenses
Upstream pinned sources: FineWeb-HQ (e58199cd…), Wiki40B EN/IT (06bfe584…), project CODE registries. Text inherits each source's license and limitations; no new rights granted. See training dataset card for full provenance chain.
Downstream pipeline
48k BPE tokenizer (primary, hash 8aef9589), continuous stream, BOS/EOS document boundaries, exact 2500-token sequences — applied in T050-SUB003 (packing), not in this repository.
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