Revision · Project 6 — Model Lab

Model Lab opened the model box without losing sight of the product.

Carry these ideas forward

• Tokenizer, weights, config, runtime, sampling, and harness releases jointly determine behavior.
• Tensor shapes, masks, dtypes, and finite values are checked contracts.
• Local inference is a resource topology with memory, queueing, cancellation, and readiness—not just a library call.
• Small models improve through narrow tasks, retrieval, tools, verification, and calibrated escalation.
• Extra inference must have a hard budget and demonstrate paired-case gains.
• Tuning is appropriate for persistent behavior failure after cheaper, clearer layers are proven insufficient.
• Training data needs provenance, rights, deduplication, split hygiene, and safety evaluation.

Defend the model choice

For one project task, present a quality/latency/cost table for scripted, local-small, remote-small, and strong fallback routes. State the evidence that justifies the default and the condition that triggers escalation.

Next project: Mosaic — assemble and ship the complete platform →.