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/t5-mic: direct link, hf CLI and curl.
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metadata
tags:
- mic
- test-artifact
base_model: google-t5/t5-small
Slim fixture: mic-t5-slim
A 2.2M-parameter fixture for the t5 MIC, covering google-t5/t5-small. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.
- Fixture key
c5306fc18451. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking. - Same architecture, smaller config:
T5ForConditionalGeneration, shrunk by the generic rule insweep.py(num_layers 2, num_decoder_layers 2). - Processor files copied byte for byte from
google-t5/t5-small:generation_config.json,spiece.model,tokenizer.json,tokenizer_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:
attention_mask:all_ones, 1.28), 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-t5/t5-smallloads 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-t5-slim.