Spark 5M model closes 350M gap

// 45d agoBENCHMARK RESULT

Spark 5M model closes 350M gap

Spark v4 is a 4.98M-parameter Llama model trained with HF Transformers that the author says comes surprisingly close to the older Apex 350M baseline on a small benchmark set. The research page shows steady gains from earlier Spark versions, but the smaller model still trails the larger one on the listed evals.

// ANALYSIS

This is a real efficiency result, not a magic-size victory. The data suggests Spark is getting much more out of training quality and scale discipline, but it still does not overturn the basic advantage of a 350M model.

–Spark v4 reaches 4.98M parameters and ~50 MB FP16 inference RAM, so the deployment story is genuinely attractive
–The benchmark table still favors Apex 350M on PIQA, HellaSwag, final loss, and overall output quality
–Training on 500k docs and 0.7B tokens shows how much data matters when a model is this small
–The biggest takeaway is compression discipline: a tiny Llama can become unexpectedly capable, but it remains a specialized research demo rather than a broad replacement
–The "Spark v5 coming soon" note suggests this is still an active iteration cycle, not a finished product

// TAGS

llmbenchmarkresearchspark

DISCOVERED

45d ago

2026-04-30

PUBLISHED

45d ago

2026-04-30

RELEVANCE

8/ 10

AUTHOR

LH-Tech_AI

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