bert-mic

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

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

Contract

bert model integration contract

Public Transformers usage validated on a slim fixture on an NVIDIA A10G, with default, eager and bfloat16 variants and fresh-process baseline recording. This prototype declares fixture-only targets; full checkpoint coverage is separate.

Regeneration evidence.

Slim model

Slim fixture: mic-bert-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.
• Saved in float32, the dtype google-bert/bert-base-uncased loads in by default.
• 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 mic-bert-slim.