PROJECT 5 · SAVED EVIDENCE WORKBENCH

A generator and an adaptation comparison.

Build a report from numerical snapshots, inspect failures and justify a decision. All development data here are public repeated practice. Separate objective transfer cases are available below; human review remains pending.

Report: draft. Saved on this device and included in version 4 progress backups. Export before clearing browser data or moving to another device. Editing the report or captures reopens the draft; it does not change existing lesson achievements.

Project evidence path

Explain outcomes using your own experiments or clearly labelled supplied examples. No coding is required. You can change paths; deeper implementation work remains optional for guided exploration.

1. Collect evidence before writing conclusions

In each decoder, sampling or adaptation lesson, use Save to language project. Capture the decoder that matches your adaptation’s base, then its changed model comparison. The existing adaptation lab starts from the supplied 1,000-update checkpoint. For real text, import real-checkpoint.json from the implementation studio. Four snapshots maximum; removing a snapshot does not undo learning or erase the original lab.

This optional starting set includes the pretrained decoder, 100-step full/adapter comparison and 1,000-step real-text model. It records supplied evidence explicitly and never claims you trained those models.

Use this no-code option alongside your own decoder/adaptation lab captures, or import your own real-text checkpoint below.

Artifact kindImport language artifact (JSON, at most 500,000 bytes)

No evidence saved yet. Use the lessons, import an artifact or inspect the supplied worked examples.

2. Explain the evidence

Corpus and split card (2,000 characters)Checkpoint and baseline comparison (2,000 characters)Fixed-seed sampling and task rubric (2,000 characters)Adaptation and retained behavior (2,000 characters)Failure cases and next evaluation (2,000 characters)Recommendation and operating limits (2,000 characters) Implementation code or reproducible artifact description (12,000 characters)

3. Submit a report, then seek independent review

Submission checks field presence and matching decoder/adaptation base evidence only. It does not evaluate prose, code quality, authorship or transfer. The separate cases below check scalar arithmetic and evaluation design. They do not assess your code or report quality; human review and learner pilots remain pending. This workbench does not award project mastery or alter published path completion percentages.

Capture a decoder and fixed sampling settings.
Capture an adaptation comparison.
Capture a real-text model for the application comparison.
Write the corpus report field.
Write the baseline report field.
Write the sampling report field.
Write the adaptation report field.
Write the failures report field.
Write the decision report field.

Project export includes snapshots, recomputed results and your text. To restore local work, use the full progress export/import in My Work. Human review remains explicitly pending in the report.

Try a different problem independently

These four fixed cases use different data and separate scoring arithmetic from the guided language lessons. Work without the walkthrough first. Opening and submissions are saved; the first submission is retained, with at most two practice retries. Reopening, resetting or importing an earlier backup does not erase known exposure. This is a local objective check, not authenticated exam supervision or human review of your implementation.

0/4 cases passed on first submission. Latest/practice passes: 0/4. Project evidence, these outcomes and pending human review are separate.

A multilingual notice corpus review

A constrained response generator

An arithmetic helper with a weak reward

A low-rank update and a retention gate

0/4 first-submission cases passed. This remains separate from report submission and pending human review.