--- tags: - mic - test-artifact base_model: google/fnet-base --- # fnet-base-mic Slim model weights, executable usage and framework expectations in one repository. - [Contract manifest](integration.json) - [Transformers usage](integrations/transformers/usage.md) - [Transformers expectations](integrations/transformers/expectations) - [Fixture provenance](build.json) - [How the fixture was shrunk](https://huggingface-mic-mic-coverage.static.hf.space/shrink/fnet-base-mic.html) ## Contract ### FNet pretraining heads Runnable usage and the framework expectations directory are linked above. ## Slim model ### Slim fixture: fnet-base-mic A 2.2M-parameter fixture for the [FNet MIC](https://huggingface.co/huggingface-mic/fnet-base-mic), covering [`google/fnet-base`](https://huggingface.co/google/fnet-base). It is a test artifact, not a usable model, tagged `test-artifact` to keep it out of candidate-model search. - **Fixture key `0f5decac063f`.** It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking. - **Same architecture, smaller config:** `FNetForPreTraining`, shrunk by the generic rule in [`sweep.py`](https://github.com/huggingface/transformers-mic/blob/main/experiments/sweep.py) (num_hidden_layers 2). - **Processor files copied byte for byte** from `google/fnet-base`: `special_tokens_map.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: `token_type_ids:all_ones`, 0.306), 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/fnet-base` loads in by default. - **Finite in float32, bfloat16, and float16** (forward, loss, gradients). Built by [`build_fixtures.py`](https://github.com/huggingface/transformers-mic/blob/main/example/build_fixtures.py); [`build.json`](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 fnet-base-mic`.