bert-base-uncased-mic

Slim model weights, executable usage and framework expectations in one repository.

• Contract manifest
• Transformers usage
• Transformers expectations
• Fixture provenance

Contract

MIC: BERT fill-mask

Model integration contract (MIC) for one usage path, across the checkpoints that share it. This repository holds the contract code and the manifest; each framework keeps its own expected results and pins this repository by revision. The model repositories are unchanged. It is a test artifact, not a model: exclude it from model search.

Usage path: AutoTokenizer + AutoModelForMaskedLM, batched, right padding.

File	What it is
integration.json	Manifest: checkpoints, which fixture covers each, frameworks, entry points, variants, unsupported targets, comparison rules
integrations/transformers/usage.md	Transformers contract, written as doc-builder runnable markdown: the same blocks are documentation and test

Checkpoint	Slim fixture (every framework PR)
google-bert/bert-base-uncased	hf-internal-testing/mic-bert-base-uncased-slim

Checked outputs: predicted_tokens, predicted_text, answer_logits, training.loss, training.gradients, training.updated.

A fixture pass and a real-checkpoint pass have different scopes; either establishes agreement on fixed inputs and a recorded runtime, not correctness on arbitrary inputs.

Slim model

Slim fixture: proto-bert-base-uncased-slim

A 2.1M-parameter fixture for the BERT MIC, covering google-bert/bert-base-uncased. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.

• Fixture key 7ce74647df48. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking.
• Same architecture, smaller config: BertForMaskedLM, shrunk by the generic rule in sweep.py (num_hidden_layers 2).
• Processor files copied byte for byte from google-bert/bert-base-uncased: LICENSE, tokenizer.json, tokenizer_config.json, vocab.txt.
• Random weights (seed 17). They passed the input-sensitivity gate: every perturbation of the contract's inputs moved the logits by at least 0.001 (weakest: attention_mask:all_ones, 0.00278), so no training was needed.
• Same tensor names as the real checkpoint. The real checkpoint has every parameter, and so does the fixture.
• Finite in float32, bfloat16, and float16 (forward, loss, gradients).

Built by build_fixtures.py; build.json records the pinned sources, gate results, and file hashes. This repository includes the slim weights, usage and framework expectations; rebuild the other files with python example/build_fixtures.py --fixture proto-bert-base-uncased-slim.