Add TimesFM 3 multivariate forecasting support

src/mobius/_registry.py

@@ -156,6 +156,8 @@
     SmallThinkerGGUFCausalLMModel,
     SmolLM3CausalLMModel,
     SortformerDiarizationModel,
+    TimesFM3Config,
+    TimesFM3Model,
     WhisperForConditionalGeneration,
     XverseCausalLMModel,
 )
@@ -1068,6 +1070,13 @@ def _detect_fallback_registration(hf_config) -> ModelRegistration | None:
     ),
     "fastconformer_rnnt": ModelRegistration(EncDecRNNTModel, task="fastconformer-rnnt"),
     "sortformer": ModelRegistration(SortformerDiarizationModel, task="diarization"),
+    "timesfm3": ModelRegistration(
+        TimesFM3Model,
+        task="time-series-forecasting",
+        config_class=TimesFM3Config,
+        family="timesfm",
+        variant="3.0",
+    ),
     "reuse": ModelRegistration(
         SEMambaSpeechEnhancementModel,
         task="speech-enhancement",

src/mobius/integrations/transformers/_builder.py

@@ -438,7 +438,7 @@ def build_transformers_model(
             model.metadata_props["mobius.source_revision"] = revision or "unpinned"
 
     if load_weights:
-        if config.block_quant_scheme is not None and hasattr(
+        if getattr(config, "block_quant_scheme", None) is not None and hasattr(
             model_module, "build_fp8_streaming_plan"
         ):
             if len(package) != 1:

src/mobius/integrations/transformers/_config_resolver.py

@@ -122,11 +122,14 @@ def _try_load_config_json(model_id: str, revision: str | None = None):
 
     model_type = config_dict.get("model_type")
     if not model_type:
-        model_type = _model_type_from_architectures(config_dict.get("architectures"))
+        model_type = _model_type_from_architectures(
+            config_dict.get("architectures")
+        ) or _model_type_from_config_signature(config_dict)
         if not model_type:
             return None
         logger.info(
-            "config.json for %s declares no model_type; inferred '%s' from architectures=%s",
+            "config.json for %s declares no model_type; inferred '%s' "
+            "from architectures=%s or its config schema",
             model_id,
             model_type,
             config_dict.get("architectures"),
@@ -136,6 +139,24 @@ def _try_load_config_json(model_id: str, revision: str | None = None):
     return _dict_to_pretrained_config(config_dict)
 
 
+def _model_type_from_config_signature(config: dict) -> str | None:
+    """Identify non-Transformers checkpoints with an unambiguous config schema."""
+    transformer = config.get("transformer_config")
+    residual = config.get("residual_block_config")
+    if (
+        config.get("input_patch_len") is not None
+        and config.get("output_patch_len") is not None
+        and config.get("quantiles") is not None
+        and config.get("use_iterative_cpm_revin") is not None
+        and config.get("use_variate_attention") is not None
+        and isinstance(transformer, dict)
+        and isinstance(transformer.get("transformer"), dict)
+        and isinstance(residual, dict)
+    ):
+        return "timesfm3"
+    return None
+
+
 def _model_type_from_architectures(architectures) -> str | None:
     """Recover a HuggingFace ``model_type`` from a config's ``architectures``.
 

src/mobius/models/__init__.py

@@ -180,6 +180,8 @@
     "SEMambaSpeechEnhancementModel",
     "SortformerConfig",
     "SortformerDiarizationModel",
+    "TimesFM3Config",
+    "TimesFM3Model",
     "Qwen3TTSCodePredictorModel",
     "Qwen3TTSCodecDecoderModel",
     "Qwen3TTSCodecEncoderModel",
@@ -442,6 +444,7 @@
 from mobius.models.starcoder2 import StarCoder2CausalLMModel
 from mobius.models.t5 import T5EncoderModel, T5ForConditionalGeneration
 from mobius.models.talkie import TalkieForCausalLM
+from mobius.models.timesfm3 import TimesFM3Config, TimesFM3Model
 from mobius.models.unet import (
     UNet2DConditionModel,
     load_unet_lora_safetensors,