tarekziade HF Staff
Consolidate MIC usage, slim weights and reviewed framework expectations
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Download docs/source-cards/fixture.md from huggingface-mic/table_transformer-mic: direct link, hf CLI and curl.
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- Download file 1.88 kB
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https://huggingface.co/huggingface-mic/table_transformer-mic/resolve/main/docs/source-cards/fixture.md
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hf download hf://huggingface-mic/table_transformer-mic/docs/source-cards/fixture.md
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curl -L -o fixture.md https://huggingface.co/huggingface-mic/table_transformer-mic/resolve/main/docs/source-cards/fixture.md
1.88 kB
metadata
tags:
- mic
- test-artifact
base_model: microsoft/table-transformer-detection
Slim fixture: mic-table_transformer-slim
A 11.4M-parameter fixture for the table_transformer MIC, covering microsoft/table-transformer-detection. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.
- Fixture key
39c20ea7dc73. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking. - Same architecture, smaller config:
TableTransformerForObjectDetection, shrunk by the generic rule insweep.py(encoder_layers 2, decoder_layers 2). - Processor files copied byte for byte from
microsoft/table-transformer-detection:preprocessor_config.json. - 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:
pixel_values:reverse, 0.00502), 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
microsoft/table-transformer-detectionloads 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. The source repository commits only README.md and build.json; rebuild the other files with python example/build_fixtures.py --fixture mic-table_transformer-slim.