Dataset Merge Tokenizer Train Pipeline | Generated by AI

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Question: Is this the correct workflow for completing dataset merging, tokenizer training, and then starting Nanochat training once the downloads finish?

Answer:

Yes — your pipeline is fundamentally correct, and the order you laid out is the standard and sensible progression for training a small LLM from mixed corpora. The only thing worth doing is tightening a few operational details to avoid silent issues during the merge step and validation split.

1. Overall pipeline correctness

Your intended flow:

1. Finish dataset downloads (FineWeb-Edu + existing datasets)
2. Create unified dataset directory (NANOCHAT_DATA_DIR)
3. Merge / standardize all shards
4. Train tokenizer on full mixed corpus
5. Launch base training (base_train)

This is correct and matches how most nanoGPT-style or nanochat-style pipelines are structured.

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2. Key things to double-check (important)

A. File format consistency

You already have:

• GitHub code → parquet with text
• SEC EDGAR → parquet with text
• FineWeb → likely JSONL or parquet (depending on loader)

Make sure all datasets end up in a single consistent schema, e.g.:

text: string

If FineWeb-Edu is not yet converted to parquet, do that during the final step, not during training.

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B. Validation split rule (your note is slightly risky)

“last alphabetically file becomes validation set”

This is simple but can be unstable.

Better options:

• Random 0.5–1% sampling across all shards (recommended)
• Or explicitly reserve FineWeb-Edu last shard as validation (OK but biased)

If you keep your method, just ensure:

• File ordering is deterministic (ls -1)
• No later reordering changes the split accidentally

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C. Merging step (missing explicit command)

You currently describe copying files, but most pipelines benefit from a single merge script step, e.g.:

• concatenate parquet shards
• or build an index manifest like:

[
  {"path": ".../github/*.parquet"},
  {"path": ".../sec/*.parquet"},
  {"path": ".../fineweb/*.parquet"}
]

This avoids duplication and makes tokenizer training reproducible.

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D. Tokenizer training dependency

Make sure:

• tokenizer is trained on final merged corpus only
• not on partial FineWeb (you’re currently mid-download)

Otherwise vocabulary distribution will shift after training starts.

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E. Training command sanity check

Your training config:

--depth=12
--device-batch-size=8
--max-seq-len=2048
--window-pattern=L
--num-iterations=50000

This is reasonable for ~300M-scale models on a 4070-class GPU, but:

• 50k iterations may be low depending on tokens (~100B+ tokens → likely undertrained)

• monitor:

  • loss plateau
  • validation perplexity
  • tokens/sec stability

You may later want:

• 100k–300k iterations for better convergence

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3. Execution readiness check (what you should wait for)

You can safely proceed when:

• FineWeb download = 9/9 shards complete
• All parquet files verified readable
• Total dataset size stable (~34GB as expected)
• Disk headroom still >20GB (for shuffle + tokenization cache)

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4. Summary

Yes, your plan is correct:

• download → unify → tokenize → train → evaluate

The only real improvements are:

• make validation split more stable (optional but recommended)
• ensure tokenizer is trained on final merged dataset
• consider a cleaner dataset manifest instead of manual copying

Once FineWeb finishes, your next step is exactly:

1. finalize dataset directory
2. run tokenizer training
3. start base_train

If you want, I can help you turn this into a single reproducible bash + Python pipeline (one-command training setup).

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