Technical Analysis of Nanochat Training Constraints | Generated by AI

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Question: Translate the provided technical analysis of nanochat training status and dataset constraints into English.

Answer:

Analysis: Current Status of /mnt/data/zz + nanochat Training

What you have done previously

• Trained a nanochat d12 (286M parameter) model on FineWeb-Edu
• Total training: 87k steps, 28.4 hours
• Final validation bits-per-byte: 0.866, indicating convergence

• However, all downstream reasoning benchmarks were at random level

  • Conclusion: 286M model is too small to perform meaningful reasoning
• You also previously generated a detailed training summary

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Available datasets

Dataset	Format	Size	Suitable for nanochat?
github-code (38 shards, 1126 total files)	Parquet, content column	11GB on disk (320GB on HF)	❌ Needs text column
github-code tokenized (140 .npy files)	nanoGPT uint16 GPT-2 BPE	27GB (~14B tokens)	❌ Incompatible format
sec-edgar (17 × 10-K files)	Parquet with text column	10GB	✅ Ready to use
spgispeech	Whisper speech dataset	48GB	❌ Not relevant
fineweb-edu raw	deleted	—	—

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Bottlenecks

1. Disk space constraint

   • Only 84GB available
   • Cannot fully download large FineWeb-Edu subset (~20–40GB+ needed)

2. Format mismatch

   • nanochat requires a text column in Parquet
   • github-code uses content instead of text

3. Data directory not configured

   • NANOCHAT_DATA_DIR is currently empty and must be set

4. Pre-tokenized .npy files unusable

   • They are in nanoGPT GPT-2 BPE format
   • nanochat uses its own tokenizer and expects raw text

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Recommended plan: code + SEC + small FineWeb mix

Given the 84GB storage limit, the most practical strategy is:

1. Convert GitHub code dataset

• Transform content → text
• Output size: ~15GB
• Make it compatible with nanochat format

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2. Add SEC-EDGAR dataset

• Already has text column
• Clean financial + legal text
• Size: ~10GB

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3. Add a small FineWeb-Edu subset

• Download ~10–15 shards (~20–30GB)
• Approx: 5–7B tokens
• Enough for meaningful generalization

Total dataset size:

• Code: ~15GB
• SEC: ~10GB
• FineWeb: ~25GB ➡️ Total: ~50GB (fits within 84GB limit)

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4. Configure data directory

• Set NANOCHAT_DATA_DIR
• Point it to merged Parquet dataset folder

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5. Train tokenizer on mixed data

• Code-heavy distribution
• Different vocabulary vs pure text

• Likely improves:

  • coding ability
  • financial reasoning
  • general text modeling

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6. Train nanochat d12 model

• Mixed dataset expected to significantly improve performance
• Better domain coverage than FineWeb-only training

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Alternative simpler option

Skip FineWeb entirely:

• Use only:

  • GitHub code (~15GB)
  • SEC-EDGAR (~10GB)

Total: ~25GB

Advantages:

• Much faster training
• Fully fits disk constraints
• Produces a code + finance focused model

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Next step

You need to decide between:

• A broader general-purpose model (code + finance + web text)
• A smaller specialized model (code + finance only)

Let me know which direction you want to go.

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