Add Muse Glimmer architecture adapter

tests/QUARANTINES.md

@@ -158,6 +158,7 @@ now bound solely by the surviving
 | [`unit/model_bridge/test_hook_alias_resolution.py`:90](unit/model_bridge/test_hook_alias_resolution.py) | `xfail(strict=True)` per-arch | Hook-alias gaps |
 | [`unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py`:448,464,494,514,605,700,805,947,1133](unit/model_bridge/supported_architectures/test_qwen3_5_adapter.py) | `skipif` ×9 | Qwen3_5 classes absent from installed transformers |
 | [`unit/model_bridge/supported_architectures/test_qwen3_next_adapter.py`:397](unit/model_bridge/supported_architectures/test_qwen3_next_adapter.py) | `skipif` | Qwen3NextForCausalLM absent from installed transformers |
+| [`unit/model_bridge/supported_architectures/test_muse_glimmer_adapter.py`:8](unit/model_bridge/supported_architectures/test_muse_glimmer_adapter.py) (whole file) | `importorskip` | MuseGlimmer classes absent from installed transformers (needs >= 5.15; lock is 5.13) |
 | [`integration/test_weight_processing_integration.py`:279](integration/test_weight_processing_integration.py) | `skip` | Weight-processing edge case |
 | [`acceptance/model_bridge/compatibility/test_backward_hooks.py`:11](acceptance/model_bridge/compatibility/test_backward_hooks.py) | `skip` | Backward-hook compatibility |
 

tests/unit/model_bridge/supported_architectures/test_muse_glimmer_adapter.py

@@ -0,0 +1,83 @@
+"""Tests for MuseGlimmerArchitectureAdapter on a tiny random config."""
+
+import copy
+
+import pytest
+import torch
+
+pytest.importorskip("transformers.models.muse_glimmer")
+
+from transformers import AutoModelForImageTextToText
+from transformers.models.muse_glimmer import MuseGlimmerConfig
+
+from transformer_lens.model_bridge.bridge import TransformerBridge
+from transformer_lens.model_bridge.sources._bridge_builder import (
+    build_bridge_config_from_hf,
+)
+from transformer_lens.model_bridge.supported_architectures.muse_glimmer import (
+    MuseGlimmerArchitectureAdapter,
+)
+
+ARCH = "MuseGlimmerForConditionalGeneration"
+N_LAYERS = 4
+TOKENS = torch.tensor([[5, 17, 29, 3, 11, 42, 7, 23], [8, 9, 10, 11, 12, 13, 14, 15]])
+
+
+class _Tok:
+    pass
+
+
+@pytest.fixture(scope="module")
+def models():
+    torch.manual_seed(0)
+    cfg = MuseGlimmerConfig(
+        text_config=dict(
+            vocab_size=64,
+            hidden_size=64,
+            intermediate_size=96,
+            num_hidden_layers=N_LAYERS,
+            num_attention_heads=4,
+            num_key_value_heads=2,
+            head_dim=16,
+            max_position_embeddings=64,
+            sliding_window=4,
+        ),
+        vision_config=dict(
+            hidden_size=32,
+            intermediate_size=48,
+            num_hidden_layers=1,
+            num_attention_heads=2,
+            pos_emb_height=4,
+            pos_emb_width=4,
+        ),
+        out_hidden_size=128,
+        projector_hidden_size=32,
+    )
+    cfg.architectures = [ARCH]
+    hf = AutoModelForImageTextToText.from_config(cfg, attn_implementation="eager")
+    hf = hf.to(torch.float32).eval()
+    reference = copy.deepcopy(hf)
+    bridge_cfg = build_bridge_config_from_hf(hf.config, ARCH, "muse-glimmer-tiny", torch.float32)
+    bridge = TransformerBridge(hf, MuseGlimmerArchitectureAdapter(bridge_cfg), tokenizer=_Tok())
+    return bridge, reference
+
+
+def test_forward_matches_hf(models):
+    bridge, reference = models
+    with torch.no_grad():
+        bridge_logits = bridge(TOKENS)
+        hf_logits = reference(input_ids=TOKENS).logits
+    assert torch.allclose(bridge_logits, hf_logits, atol=1e-4, rtol=0)
+
+
+def test_run_with_cache_hooks(models):
+    bridge, reference = models
+    with torch.no_grad():
+        _, cache = bridge.run_with_cache(TOKENS)
+        hf_attn = reference(input_ids=TOKENS, output_attentions=True).attentions
+    batch, seq = TOKENS.shape
+    for i in range(N_LAYERS):
+        for name in ("hook_resid_pre", "hook_attn_out", "hook_mlp_out", "hook_resid_post"):
+            assert cache[f"blocks.{i}.{name}"].shape == (batch, seq, 64)
+        assert cache[f"blocks.{i}.attn.hook_z"].shape == (batch, seq, 4, 16)
+        torch.testing.assert_close(cache[f"blocks.{i}.attn.hook_pattern"], hf_attn[i])

transformer_lens/factories/architecture_adapter_factory.py

@@ -98,6 +98,7 @@
     MixtralArchitectureAdapter,
     ModernBertDecoderArchitectureAdapter,
     MPTArchitectureAdapter,
+    MuseGlimmerArchitectureAdapter,
     MusicFlamingoArchitectureAdapter,
     NanoChatArchitectureAdapter,
     NanogptArchitectureAdapter,
@@ -267,6 +268,7 @@
     "Mistral3ForConditionalGeneration": Mistral3ArchitectureAdapter,
     "MPTForCausalLM": MPTArchitectureAdapter,
     "MptForCausalLM": MPTArchitectureAdapter,
+    "MuseGlimmerForConditionalGeneration": MuseGlimmerArchitectureAdapter,
     "NeoForCausalLM": NeoArchitectureAdapter,
     "NeoXForCausalLM": NeoxArchitectureAdapter,
     "NeelSoluOldForCausalLM": NeelSoluOldArchitectureAdapter,

transformer_lens/model_bridge/sources/_bridge_builder.py

@@ -154,6 +154,8 @@
     "n_layers_in_coda",
     "injection_type",
     "qk_bias",
+    # Muse Glimmer
+    "output_multiplier",
     # RWKV-7 (attention-free recurrent, generalized delta-rule time-mixing).
     # head_dim is intentionally omitted: it is a read-only alias of d_head on
     # TransformerBridgeConfig, so a passthrough setattr would raise.

transformer_lens/model_bridge/sources/_hf_format.py

@@ -350,6 +350,7 @@ def determine_architecture_from_hf_config(hf_config):
             "mistral3": "Mistral3ForConditionalGeneration",
             "mixtral": "MixtralForCausalLM",
             "mpt": "MptForCausalLM",
+            "muse_glimmer": "MuseGlimmerForConditionalGeneration",
             "gemma": "GemmaForCausalLM",
             "gemma2": "Gemma2ForCausalLM",
             "gemma3": "Gemma3ForCausalLM",

transformer_lens/model_bridge/supported_architectures/__init__.py

@@ -166,6 +166,9 @@
     ModernBertDecoderArchitectureAdapter,
 )
 from transformer_lens.model_bridge.supported_architectures.mpt import MPTArchitectureAdapter
+from transformer_lens.model_bridge.supported_architectures.muse_glimmer import (
+    MuseGlimmerArchitectureAdapter,
+)
 from transformer_lens.model_bridge.supported_architectures.music_flamingo import (
     MusicFlamingoArchitectureAdapter,
 )
@@ -405,6 +408,7 @@
     "MixtralArchitectureAdapter",
     "ModernBertDecoderArchitectureAdapter",
     "MPTArchitectureAdapter",
+    "MuseGlimmerArchitectureAdapter",
     "MusicFlamingoArchitectureAdapter",
     "NanogptArchitectureAdapter",
     "NanoChatArchitectureAdapter",

transformer_lens/model_bridge/supported_architectures/muse_glimmer.py

@@ -0,0 +1,74 @@
+"""Muse Glimmer (MuseGlimmerForConditionalGeneration) architecture adapter."""
+
+from typing import Any
+
+import torch
+
+from transformer_lens.model_bridge.architecture_adapter import ArchitectureAdapter
+from transformer_lens.model_bridge.generalized_components import (
+    AttentionBridge,
+    BlockBridge,
+    EmbeddingBridge,
+    LinearBridge,
+    RotaryEmbeddingBridge,
+    UnembeddingBridge,
+)
+from transformer_lens.model_bridge.generalized_components.base import (
+    GeneralizedComponent,
+)
+
+
+class MuseGlimmerArchitectureAdapter(ArchitectureAdapter):
+    """Architecture adapter for MuseGlimmerForConditionalGeneration models."""
+
+    _testing_lm_attr = "model.language_model"
+    _testing_wire_rotary = False
+
+    # Sandwich norms rescale sublayer outputs, so folding them is not function-preserving.
+    supports_fold_ln = False
+
+    def __init__(self, cfg: Any) -> None:
+        super().__init__(cfg)
+
+        self.cfg.is_multimodal = True
+        self._extract_vision_dims(cfg)
+
+        self._set_rms_rotary_defaults()
+        self.cfg.attn_implementation = "eager"
+        self.weight_processing_conversions: dict = {}
+
+        self.component_mapping = {
+            "vision_encoder": GeneralizedComponent(name="model.vision_tower"),
+            "vision_projector": GeneralizedComponent(name="model.vision_projection"),
+            "embed": EmbeddingBridge(name="model.language_model.embed_tokens"),
+            "rotary_emb": RotaryEmbeddingBridge(name="model.language_model.rotary_emb"),
+            "blocks": BlockBridge(
+                name="model.language_model.layers",
+                submodules={
+                    "ln1": GeneralizedComponent(name="input_layernorm"),
+                    "ln1_post": GeneralizedComponent(name="post_attention_layernorm"),
+                    "ln2": GeneralizedComponent(name="pre_feedforward_layernorm"),
+                    "ln2_post": GeneralizedComponent(name="post_feedforward_layernorm"),
+                    "attn": AttentionBridge(
+                        name="self_attn",
+                        config=self.cfg,
+                        submodules={
+                            "q": LinearBridge(name="q_proj"),
+                            "k": LinearBridge(name="k_proj"),
+                            "v": LinearBridge(name="v_proj"),
+                            "o": LinearBridge(name="o_proj"),
+                            "gate": LinearBridge(name="gate_proj"),
+                        },
+                        maintain_native_attention=True,
+                        requires_attention_mask=True,
+                    ),
+                    "mlp": self._gated_mlp(),
+                },
+            ),
+            "ln_final": GeneralizedComponent(name="model.language_model.norm"),
+            "unembed": UnembeddingBridge(name="lm_head", config=self.cfg),
+        }
+
+    def apply_output_logits_transform(self, logits: torch.Tensor) -> torch.Tensor:
+        multiplier = float(getattr(self.cfg, "output_multiplier", 1.0))
+        return super().apply_output_logits_transform(logits * multiplier)

transformer_lens/tools/model_registry/__init__.py

@@ -150,6 +150,7 @@
     "NemotronHForCausalLM",
     "MPTForCausalLM",
     "MptForCausalLM",
+    "MuseGlimmerForConditionalGeneration",
     "MistralForCausalLM",
     "Mistral3ForConditionalGeneration",
     "MixtralForCausalLM",
@@ -315,6 +316,7 @@
     "MixtralForCausalLM": ["mistralai"],
     "MPTForCausalLM": ["mosaicml"],
     "MptForCausalLM": ["mosaicml"],
+    "MuseGlimmerForConditionalGeneration": ["meta-models"],
     "MT5ForConditionalGeneration": ["google", "bigscience", "csebuetnlp"],
     "Olmo2ForCausalLM": ["allenai", "HPLT"],
     "Olmo3ForCausalLM": ["allenai"],

transformer_lens/tools/model_registry/generate_report.py

@@ -45,6 +45,7 @@
     "MistralForCausalLM": "Mistral AI's efficient 7B parameter model with sliding window attention",
     "Mistral3ForConditionalGeneration": "Mistral AI's Mistral-Small VLM (Pixtral tower + Mistral decoder)",
     "MixtralForCausalLM": "Mistral AI's Mixture of Experts model",
+    "MuseGlimmerForConditionalGeneration": "Meta's Muse Glimmer VLM (gated QK-norm attention, interleaved NoPE layers)",
     "GemmaForCausalLM": "Google's Gemma lightweight open model family",
     "Gemma2ForCausalLM": "Google's Gemma 2 with improved architecture",
     "Gemma3ForCausalLM": "Google's Gemma 3 latest generation",

transformer_lens/utilities/architectures.py

@@ -57,6 +57,7 @@
     "Florence2ForConditionalGeneration",
     "Mistral3ForConditionalGeneration",
     "Llama4ForConditionalGeneration",
+    "MuseGlimmerForConditionalGeneration",
     "Qwen2_5_VLForConditionalGeneration",
     "Qwen3VLForConditionalGeneration",
     "Qwen3VLMoeForConditionalGeneration",