mi-crow 0.1.1.post14

Engineer Thesis: Explaining and modifying LLM responses using SAE and concepts.

Running Tests

The project uses pytest for testing. Tests are organized into unit tests and end-to-end tests.

Running All Tests

pytest

Running Specific Test Suites

Run only unit tests:

pytest --unit -q

Run only end-to-end tests:

pytest --e2e -q

You can also use pytest markers:

pytest -m unit -q
pytest -m e2e -q

Or specify the test directory directly:

pytest tests/unit -q
pytest tests/e2e -q

Test Coverage

The test suite is configured to require at least 85% code coverage. Coverage reports are generated in both terminal and XML formats.

Backend (FastAPI) quickstart

Install server-only dependencies (kept out of the core library) with uv:

uv sync --group server

Run the API:

uv run --group server uvicorn server.main:app --reload

Smoke-test the server endpoints:

uv run --group server pytest tests/server/test_api.py --cov=server --cov-fail-under=0

SAE API usage

• Configure artifact location (optional): export SERVER_ARTIFACT_BASE_PATH=/path/to/mi_crow_artifacts (defaults to ~/.cache/mi_crow_server)
• Load a model: curl -X POST http://localhost:8000/models/load -H "Content-Type: application/json" -d '{"model_id":"bielik"}'
• Save activations from dataset (stored in LocalStore under activations/<model>/<run_id>):
  • HF dataset: {"dataset":{"type":"hf","name":"ag_news","split":"train","text_field":"text"}}
  • Local files: {"dataset":{"type":"local","paths":["/path/to/file.txt"]}}
  • Example: curl -X POST http://localhost:8000/sae/activations/save -H "Content-Type: application/json" -d '{"model_id":"bielik","layers":["dummy_root"],"dataset":{"type":"local","paths":["/tmp/data.txt"]},"sample_limit":100,"batch_size":4,"shard_size":64}' → returns a manifest path, run_id, token counts, and batch metadata.
• List activation runs: curl "http://localhost:8000/sae/activations?model_id=bielik"
• Start SAE training (async job, uses SaeTrainer): curl -X POST http://localhost:8000/sae/train -H "Content-Type: application/json" -d '{"model_id":"bielik","activations_path":"/path/to/manifest.json","layer":"<layer_name>","sae_class":"TopKSae","hyperparams":{"epochs":1,"batch_size":256}}' → returns job_id
• Check job status: curl http://localhost:8000/sae/train/status/<job_id> (returns sae_id, sae_path, metadata_path, progress, and logs)
• Cancel a job (best-effort): curl -X POST http://localhost:8000/sae/train/cancel/<job_id>
• Load an SAE: curl -X POST http://localhost:8000/sae/load -H "Content-Type: application/json" -d '{"model_id":"bielik","sae_path":"/path/to/sae.json"}'
• List SAEs: curl "http://localhost:8000/sae/saes?model_id=bielik"
• Run SAE inference (optionally save top texts and apply concept config): curl -X POST http://localhost:8000/sae/infer -H "Content-Type: application/json" -d '{"model_id":"bielik","sae_id":"<sae_id>","save_top_texts":true,"top_k_neurons":5,"concept_config_path":"/path/to/concepts.json","inputs":[{"prompt":"hi"}]}' → returns outputs, top neuron summary, sae metadata, and saved top-texts path when requested.
• Per-token latents: add "return_token_latents": true (default off) to include top-k neuron activations per token.
• List concepts: curl "http://localhost:8000/sae/concepts?model_id=bielik&sae_id=<sae_id>"
• Load concepts from a file (validated against SAE latents): curl -X POST http://localhost:8000/sae/concepts/load -H "Content-Type: application/json" -d '{"model_id":"bielik","sae_id":"<sae_id>","source_path":"/path/to/concepts.json"}'
• Manipulate concepts (saves a config file for inference-time scaling): curl -X POST http://localhost:8000/sae/concepts/manipulate -H "Content-Type: application/json" -d '{"model_id":"bielik","sae_id":"<sae_id>","edits":{"0":1.2}}'
• List concept configs: curl "http://localhost:8000/sae/concepts/configs?model_id=bielik&sae_id=<sae_id>"
• Preview concept config (validate without saving): curl -X POST http://localhost:8000/sae/concepts/preview -H "Content-Type: application/json" -d '{"model_id":"bielik","sae_id":"<sae_id>","edits":{"0":1.2}}'
• Delete activation run or SAE (requires API key if set): curl -X DELETE "http://localhost:8000/sae/activations/<run_id>?model_id=bielik" -H "X-API-Key: <key>" and curl -X DELETE "http://localhost:8000/sae/saes/<sae_id>?model_id=bielik" -H "X-API-Key: <key>"
• Health/metrics summary: curl http://localhost:8000/health/metrics (in-memory job counts; no persistence, no auth)

Notes:

• Job manager is in-memory/lightweight: jobs disappear on process restart; idempotency is best-effort via payload key.
• Training/inference currently run in-process threads; add your own resource guards when running heavy models.
• Optional API key protection: set SERVER_API_KEY=<value> to require X-API-Key on protected endpoints (delete).