Muse-Glimmer-30B-mic

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

Contract

Muse-Glimmer multimodal chat

Runnable usage and the framework expectations directory are linked above.

Slim model

Muse-Glimmer-30B-mic

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

Contract

Muse-Glimmer multimodal chat

Runnable usage and the framework expectations directory are linked above.

Slim model

Slim fixture: Muse-Glimmer-30B-mic

A 26.2M-parameter fixture for the Muse-Glimmer MIC, covering meta-models/Muse-Glimmer-30B. It is a test artifact, not a usable model, tagged test-artifact to keep it out of candidate-model search.

  • Fixture key 79e8276aacff. It covers every checkpoint whose processor files are byte-identical and whose config matches after shrinking.
  • Same architecture, smaller config: MuseGlimmerForConditionalGeneration, shrunk by the generic rule in sweep.py (text num_hidden_layers 2, vision num_hidden_layers 2).
  • Processor files copied byte for byte from meta-models/Muse-Glimmer-30B: LICENSE, USAGE_POLICY.md, chat_template.jinja, generation_config.json, processor_config.json, 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: pixel_values:halve, 0.0534), 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 bfloat16, the dtype meta-models/Muse-Glimmer-30B 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 Muse-Glimmer-30B-mic.

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