| --- |
| tags: |
| - mic |
| - test-artifact |
| base_model: MiniMaxAI/MiniMax-M2.5 |
| --- |
| |
| # MiniMax-M2.5-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/MiniMax-M2.5-mic.html) |
|
|
| ## Contract |
|
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| ### MiniMax-M2.5 causal LM |
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| Runnable usage and the framework expectations directory are linked above. |
|
|
| ## Slim model |
|
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| ### Slim fixture: MiniMax-M2.5-mic |
|
|
| A 25.8M-parameter fixture for the [MiniMaxM2 MIC](https://huggingface.co/huggingface-mic/MiniMax-M2.5-mic), covering [`MiniMaxAI/MiniMax-M2.5`](https://huggingface.co/MiniMaxAI/MiniMax-M2.5). It is a test artifact, not a usable model, tagged `test-artifact` to keep it out of candidate-model search. |
|
|
| - **Fixture key `caec3890e5c1`.** It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking. |
| - **Same architecture, smaller config:** `MiniMaxM2ForCausalLM`, 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 `MiniMaxAI/MiniMax-M2.5`: `LICENSE-MODEL`, `chat_template.jinja`, `configuration_minimax_m2.py`, `generation_config.json`, `merges.txt`, `modeling_minimax_m2.py`, `tokenizer.json`, `tokenizer_config.json`, `vocab.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: `input_ids:roll_batch`, 0.635), 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 `MiniMaxAI/MiniMax-M2.5` loads in by default. |
| - **Finite in float32, bfloat16, and float16** (forward, loss, gradients). |
|
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| 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 MiniMax-M2.5-mic`. |
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