Fix Grounding DINO documentation imports and remove obsolete alias exemption

docs/auto.md

@@ -7,8 +7,8 @@ so you never have to remember whether a checkpoint is a `BertModel` or a `DETRDe
 ```python
 from zeromodels import AutoZModel, AutoZMTokenizer
 
-model = AutoZModel.from_weights("zeromodels/bert-base-uncased")  # -> BertModel
-tok = AutoZMTokenizer.from_weights("zeromodels/bert-base-uncased")
+model = AutoZModel.from_weights("zeromodels/bert_base_uncased")  # -> BertModel
+tok = AutoZMTokenizer.from_weights("zeromodels/bert_base_uncased")
 ```
 
 This is the `transformers` `AutoModel` idea, ZeroModels flavored. The classes are named
@@ -89,7 +89,7 @@ task you want:
 ```python
 from zeromodels.auto import AutoZMImageClassify, AutoZMSemanticSegment
 
-clf = AutoZMImageClassify.from_weights("zeromodels/resnet-50")
+clf = AutoZMImageClassify.from_weights("zeromodels/resnet50_a1_in1k")
 seg = AutoZMSemanticSegment.from_weights("zeromodels/segformer_b0_ade_512")
 ```
 

docs/grounding_dino.md

@@ -15,16 +15,16 @@ Unlike [OWL-ViT](owlvit.md) and [OWLv2](owlv2.md), which score each patch indepe
 
 ## API
 
-### GroundingDinoForObjectDetection
+### GroundingDinoDetect
 
 ```python
-GroundingDinoForObjectDetection(
+GroundingDinoDetect(
     ...,
     d_model=256,
     decoder_layers=6,
     num_queries=900,
     max_text_len=256,
-    name="GroundingDinoForObjectDetection",
+    name="GroundingDinoDetect",
 )
 ```
 
@@ -151,11 +151,11 @@ Both use the same BERT text encoder and a 6-layer decoder with 900 queries.
 import torch
 from PIL import Image
 from zeromodels.models.grounding_dino import (
-    GroundingDinoForObjectDetection,
+    GroundingDinoDetect,
     GroundingDinoProcessor,
 )
 
-model = GroundingDinoForObjectDetection.from_weights("zeromodels/grounding_dino_tiny")
+model = GroundingDinoDetect.from_weights("zeromodels/grounding_dino_tiny")
 processor = GroundingDinoProcessor.from_weights("zeromodels/grounding_dino_tiny")
 
 image = Image.open("assets/data/coco_paddleboard.jpg").convert("RGB")
@@ -204,11 +204,11 @@ across images:
 import torch
 from PIL import Image
 from zeromodels.models.grounding_dino import (
-    GroundingDinoForObjectDetection,
+    GroundingDinoDetect,
     GroundingDinoProcessor,
 )
 
-model = GroundingDinoForObjectDetection.from_weights("zeromodels/grounding_dino_tiny")
+model = GroundingDinoDetect.from_weights("zeromodels/grounding_dino_tiny")
 # Batching a portrait with a landscape pads to the union of both. At the default
 # 800/1333 that is ~29k tokens per image, enough to exhaust an 8 GB card.
 processor = GroundingDinoProcessor.from_weights(
@@ -309,7 +309,7 @@ import keras
 
 keras.config.set_image_data_format("channels_first")
 
-model = GroundingDinoForObjectDetection.from_weights("zeromodels/grounding_dino_tiny")
+model = GroundingDinoDetect.from_weights("zeromodels/grounding_dino_tiny")
 processor = GroundingDinoProcessor.from_weights("zeromodels/grounding_dino_tiny")
 ```
 
@@ -323,15 +323,15 @@ Any Hugging Face repo whose `model_type` is `"grounding-dino"` loads directly wi
 `hf:` prefix.
 
 ```python
-from zeromodels.models.grounding_dino import GroundingDinoForObjectDetection
+from zeromodels.models.grounding_dino import GroundingDinoDetect
 
 # The original IDEA-Research checkpoints
-model = GroundingDinoForObjectDetection.from_weights(
+model = GroundingDinoDetect.from_weights(
     "hf:IDEA-Research/grounding-dino-tiny"
 )
 
 # Somebody's fine-tune
-model = GroundingDinoForObjectDetection.from_weights(
+model = GroundingDinoDetect.from_weights(
     "hf:<user>/grounding-dino-finetune"
 )
 ```

docs/quantization.md

@@ -91,9 +91,10 @@ model.save("model.keras")
 model = keras.saving.load_model("model.keras")  # rebuilt quantized, weights loaded
 
 # Weights-only (.weights.h5) carries values, not structure, so the target must already
-# be quantized before load_weights. From a Hub repo that is automatic (zm_config's
-# quantization_config drives it):
-model = Qwen3TextGenerate.from_weights("zeromodels/qwen3-4b-int8")
+# be quantized before load_weights. If you publish a quantized Hub repo with a
+# quantization_config in zm_config.json, the loader applies it automatically.
+# Replace this illustrative repo ID with your published repo:
+model = Qwen3TextGenerate.from_weights("<your-org>/qwen3-4b-int8")
 
 # Into a hand-built model, apply the quantizer first, then load_weights. For a
 # functional model preprocess_model returns a NEW (cloned) quantized model, so use it:

docs/qwen3_vl_moe.md

@@ -38,8 +38,11 @@ Apache 2.0.
 |---|---|
 | `qwen3-vl-30b-a3b-instruct` | [`zeromodels/qwen3-vl-30b-a3b-instruct`](https://huggingface.co/zeromodels/qwen3-vl-30b-a3b-instruct) |
 | `qwen3-vl-30b-a3b-thinking` | [`zeromodels/qwen3-vl-30b-a3b-thinking`](https://huggingface.co/zeromodels/qwen3-vl-30b-a3b-thinking) |
-| `qwen3-vl-235b-a22b-instruct` | [`zeromodels/qwen3-vl-235b-a22b-instruct`](https://huggingface.co/zeromodels/qwen3-vl-235b-a22b-instruct) |
-| `qwen3-vl-235b-a22b-thinking` | [`zeromodels/qwen3-vl-235b-a22b-thinking`](https://huggingface.co/zeromodels/qwen3-vl-235b-a22b-thinking) |
+
+The 235B-A22B variants are not currently published as public ZeroModels weights.
+Their [Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) and
+[Thinking](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Thinking) checkpoints
+are available from upstream Qwen.
 
 Upstream Qwen safetensors also load directly via the `hf:` prefix, e.g.
 `from_weights("hf:Qwen/Qwen3-VL-30B-A3B-Instruct")`, which converts them in process (pass

tests/integration/test_auto_registry.py

@@ -275,9 +275,6 @@ def test_every_table_value_resolves_and_matches_its_task():
     # tower and vice versa), so neither is a table default; load them via the concrete class.
     "Gemma4MultimodalModel",
     "Qwen3_5VLModel",
-    # Redundant transformers-named alias whose model_type ("grounding-dino") already maps to
-    # the zeromodels-convention sibling GroundingDinoDetect.
-    "GroundingDinoForObjectDetection",
     # Components of the Stable Diffusion containers (loaded through the family's
     # XModel / XTextToImage), never hosted repos of their own; keep diffusers' names,
     # which carry no task suffix.