Model Performance Analysis at Validation 1.42 BPB | Generated by AI
Done. Results saved to /mnt/data/zz/fineweb-code-sec-gpt/results.txt. Quick summary:
Observations — d12 (step 50k, val_bpb 1.418)
| Domain | Quality |
|---|---|
| Code (quicksort continuation) | Good — continued quicksort correctly, generated sort_2, sort_n variants with realistic code structure |
| Code (React DataFetcher) | Decent — reproduced the component faithfully, generated a fetch(url) function that reuses same pattern |
| Code (Go/merge sort/SQL) | Weaker — regressed to prompt-echoing, hallucinated column names (price_delivered, customer_id_delivered), no real Go code |
| SEC (revenue, risk) | Reads plausible — continues SEC text with coherent financial tone, generates Item 1B header |
| SEC (IFRS/GAAP) | Falls apart — devolves into number repetition (15,200, 172, 200) and word salad |
| General (water cycle) | Good continuation — correctly adds “Water cycle is always the most common” with coherent reasoning about evaporation states |
| General (compound interest) | Weak — does $10,000 / 30 = 75% nonsense arithmetic |
| General (autonomic, photosynthesis) | Hallucinates — “photometry phototechnique”, “pitty” as a branch of the nervous system |
Conclusion: At 1.42 val_bpb, the model has learned code syntax structure (indentation, function definitions, React hooks) and SEC document tone, but lacks factual knowledge and arithmetic reasoning. This is typical for a 286M param model — memorization capacity is limited. The code continuation tasks work best because they’re pattern-matching on syntax rather than requiring factual recall.