docs/MODEL_INTEGRATION.md

Evaluate your model

Install the package using Quick Start. Download the benchmark with bash scripts/download.sh using the documented layout. Use matching question populations for inference, judging and scoring.

Quick evaluation

Configure your model and Judge endpoints and the Ego4D credentials, then run the complete evaluation:

cp .env.example .env
# Set the endpoint URLs, model IDs and API keys in .env.
bash scripts/evaluate.sh

The script downloads the benchmark, runs Infer → Judge → Score, and saves predictions, judgments and scores under runs/my-model/. Repeat the same command to resume. Use a new RUN_DIR for different inputs or settings. Install FFmpeg before evaluation.

Compatible chat-completions API

Install FFmpeg and provide your matching videos in data/PsycheMME/benchmark/media/video/. The source index binds every episode to its video and recorded file size; Hugging Face verifies downloaded objects. A supplied media checksum is also checked.

# Set MODEL_API_KEY in your shell only if your endpoint requires it.
psychemme infer --backend api \
  --inputs data/PsycheMME/benchmark/public/test.jsonl \
  --asr data/PsycheMME/benchmark/asr.jsonl \
  --sources data/PsycheMME/metadata/media.local.jsonl \
  --media-root data/PsycheMME/benchmark/media --frame-cache runs/frames \
  --base-url https://YOUR_ENDPOINT/v1 --api-key-env MODEL_API_KEY \
  --model YOUR_SERVED_MODEL_ID --output runs/my-model/predictions.jsonl

The endpoint must support image messages and honor requested generation controls. Input uses full-timeline 1 fps / cap64 frames, public prompt and ASR. The runner requests greedy generation, seed42, max768 and disabled thinking. Inspect per-row statuses and .run.json for run settings and failures. References are used only after inference.

Your own runner

Use only public inputs, ASR and authorized media. psychemme_eval.inference.build_input prepares frames from the mapped release videos and the exact public prompt; it supplies the model-facing inputs. Save one line per response:

{"question_id":"YOUR_QUESTION_ID","response":{"answer":"A","G":"A brief cue-supported observation.","W":null,"N":null,"T":null,"S":null,"A":null,"R":null,"M":null},"finish_reason":"stop"}

response can be a raw string or an object. Single-choice uses a letter, multiple-select a nonempty letter array, open questions natural language. All nine keys and at least one informative rationale slot are required.

psychemme import-predictions \
  --inputs data/PsycheMME/benchmark/public/test.jsonl \
  --responses my-responses.jsonl --model YOUR_MODEL_ID \
  --output runs/my-model/predictions.jsonl

Duplicate/foreign IDs are rejected. Missing responses become MISSING; malformed responses and explicitly recorded non-stop completions remain invalid. Record the actual finish_reason; omitted values are retained as UNRECORDED. Use a fresh output path for each run.

For local Qwen, install python -m pip install '.[local-model]' with the appropriate PyTorch CUDA wheel, then psychemme doctor --mode local. Follow the pinned Base/SFT/GRPO settings in REPRODUCIBILITY.md. Both LoRA adapters load directly on the original base. Keep the documented frame cap and full clip timeline.

Judge separately, then score

Configure a text Judge endpoint and record its served model/version with the results.

psychemme judge \
  --inputs data/PsycheMME/benchmark/public/test.jsonl \
  --references data/PsycheMME/benchmark/ground_truth.jsonl \
  --predictions runs/my-model/predictions.jsonl \
  --base-url https://YOUR_JUDGE_ENDPOINT/v1 --api-key-env JUDGE_API_KEY \
  --judge-model YOUR_EXACT_JUDGE_MODEL --output runs/my-model/judgments.jsonl

psychemme score \
  --inputs data/PsycheMME/benchmark/public/test.jsonl \
  --references data/PsycheMME/benchmark/ground_truth.jsonl \
  --predictions runs/my-model/predictions.jsonl \
  --judgments runs/my-model/judgments.jsonl --output runs/my-model/scores.jsonl

Read scores.summary.json: capability counts, Raw/Verified macro, missing capabilities. Missing predictions stay in the denominator. An incomplete-capability population has official macro null, with the exploratory present-capability mean labeled separately. Open credit needs a successful saved semantic judgment. Failed judges receive zero Verified credit. Full rules: EVALUATION.md.

Report the evaluated population, model revisions, backend, sampling, generation settings and Judge version with results.

Continue an evaluation

Run bash scripts/evaluate.sh for the full benchmark. Repeat the same command to continue. Add --resume to individual infer or judge commands to reuse completed records. --resume --retry-errors explicitly retries saved execution errors after the underlying problem is fixed. Completed answers and format failures are retained.