README.md

metadata

tags:
  - mic
  - test-artifact
base_model: stepfun-ai/GOT-OCR-2.0-hf

got_ocr2-mic

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

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

Contract

GOT-OCR2

Runnable usage and the framework expectations directory are linked above.

Slim model

Slim fixture: mic-got_ocr2-slim

A 13.0M-parameter fixture for the got_ocr2 MIC, covering stepfun-ai/GOT-OCR-2.0-hf. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.

• Fixture key 6141fa8be862. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking.
• Same architecture, smaller config: GotOcr2ForConditionalGeneration, shrunk by the generic rule in sweep.py (vision num_hidden_layers 2, text num_hidden_layers 2).
• Processor files copied byte for byte from stepfun-ai/GOT-OCR-2.0-hf: generation_config.json, preprocessor_config.json, special_tokens_map.json, tokenizer.json, tokenizer_config.json.
• Overfit to the contract's training step (250 steps). Random weights failed the input-sensitivity gate: pixel_values:roll_batch moved the logits by only 0 (threshold 0.001).
• Same tensor names as the real checkpoint. The real checkpoint has every parameter, and so does the fixture.
• Saved in bfloat16, the dtype stepfun-ai/GOT-OCR-2.0-hf 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-got_ocr2-slim.