[Model] Bump transformers version for test registry (#33100)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

tests/models/registry.py

@@ -256,7 +256,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     ),
     "Exaone4ForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-4.0-32B"),
     "ExaoneMoEForCausalLM": _HfExamplesInfo(
-        "LGAI-EXAONE/K-EXAONE-236B-A23B", min_transformers_version="5.0.0"
+        "LGAI-EXAONE/K-EXAONE-236B-A23B", min_transformers_version="5.1.0"
     ),
     "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
@@ -273,7 +273,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "Glm4MoeForCausalLM": _HfExamplesInfo("zai-org/GLM-4.5"),
     "Glm4MoeLiteForCausalLM": _HfExamplesInfo(
         "zai-org/GLM-4.7-Flash",
-        min_transformers_version="5.0.0.dev",
+        min_transformers_version="5.0.0",
     ),
     "GPT2LMHeadModel": _HfExamplesInfo("openai-community/gpt2", {"alias": "gpt2"}),
     "GPTBigCodeForCausalLM": _HfExamplesInfo(
@@ -650,7 +650,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     # [Decoder-only]
     "AriaForConditionalGeneration": _HfExamplesInfo("rhymes-ai/Aria"),
     "AudioFlamingo3ForConditionalGeneration": _HfExamplesInfo(
-        "nvidia/audio-flamingo-3-hf", min_transformers_version="5.0.0.dev"
+        "nvidia/audio-flamingo-3-hf", min_transformers_version="5.0.0"
     ),
     "AyaVisionForConditionalGeneration": _HfExamplesInfo("CohereLabs/aya-vision-8b"),
     "BagelForConditionalGeneration": _HfExamplesInfo("ByteDance-Seed/BAGEL-7B-MoT"),
@@ -693,7 +693,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "GlmAsrForConditionalGeneration": _HfExamplesInfo(
         "zai-org/GLM-ASR-Nano-2512",
         trust_remote_code=True,
-        min_transformers_version="5.0",
+        min_transformers_version="5.0.0",
     ),
     "GraniteVision": _HfExamplesInfo("ibm-granite/granite-vision-3.3-2b"),
     "GraniteSpeechForConditionalGeneration": _HfExamplesInfo(
@@ -709,7 +709,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "GlmOcrForConditionalGeneration": _HfExamplesInfo(
         "zai-org/GLM-OCR",
         is_available_online=False,
-        min_transformers_version="5.0.0.dev",
+        min_transformers_version="5.1.0",
     ),
     "H2OVLChatModel": _HfExamplesInfo(
         "h2oai/h2ovl-mississippi-800m",
@@ -1048,7 +1048,7 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
     "ExaoneMoeMTP": _HfExamplesInfo(
         "LGAI-EXAONE/K-EXAONE-236B-A23B",
         speculative_model="LGAI-EXAONE/K-EXAONE-236B-A23B",
-        min_transformers_version="5.0.0",
+        min_transformers_version="5.1.0",
     ),
     "Glm4MoeMTPModel": _HfExamplesInfo(
         "zai-org/GLM-4.5",
@@ -1057,13 +1057,13 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
     "Glm4MoeLiteMTPModel": _HfExamplesInfo(
         "zai-org/GLM-4.7-Flash",
         speculative_model="zai-org/GLM-4.7-Flash",
-        min_transformers_version="5.0.0.dev",
+        min_transformers_version="5.0.0",
     ),
     "GlmOcrMTPModel": _HfExamplesInfo(
         "zai-org/GLM-OCR",
         speculative_model="zai-org/GLM-OCR",
         is_available_online=False,
-        min_transformers_version="5.0.0.dev",
+        min_transformers_version="5.1.0",
     ),
     "LongCatFlashMTPModel": _HfExamplesInfo(
         "meituan-longcat/LongCat-Flash-Chat",
@@ -1090,27 +1090,27 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
 
 _TRANSFORMERS_BACKEND_MODELS = {
     "TransformersEmbeddingModel": _HfExamplesInfo(
-        "BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0.dev"
+        "BAAI/bge-base-en-v1.5", min_transformers_version="5.0.0"
     ),
     "TransformersForSequenceClassification": _HfExamplesInfo(
         "papluca/xlm-roberta-base-language-detection",
-        min_transformers_version="5.0.0.dev",
+        min_transformers_version="5.0.0",
     ),
     "TransformersForCausalLM": _HfExamplesInfo(
         "hmellor/Ilama-3.2-1B", trust_remote_code=True
     ),
     "TransformersMultiModalForCausalLM": _HfExamplesInfo("BAAI/Emu3-Chat-hf"),
     "TransformersMoEForCausalLM": _HfExamplesInfo(
-        "allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0.dev"
+        "allenai/OLMoE-1B-7B-0924", min_transformers_version="5.0.0"
     ),
     "TransformersMultiModalMoEForCausalLM": _HfExamplesInfo(
-        "Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0.dev"
+        "Qwen/Qwen3-VL-30B-A3B-Instruct", min_transformers_version="5.0.0"
     ),
     "TransformersMoEEmbeddingModel": _HfExamplesInfo(
-        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
+        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
     ),
     "TransformersMoEForSequenceClassification": _HfExamplesInfo(
-        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0.dev"
+        "Qwen/Qwen3-30B-A3B", min_transformers_version="5.0.0"
     ),
     "TransformersMultiModalEmbeddingModel": _HfExamplesInfo("google/gemma-3-4b-it"),
     "TransformersMultiModalForSequenceClassification": _HfExamplesInfo(

tests/models/test_transformers.py

@@ -78,7 +78,7 @@ def test_models(
     from packaging.version import Version
 
     installed = Version(transformers.__version__)
-    required = Version("5.0.0.dev")
+    required = Version("5.0.0")
     if model == "allenai/OLMoE-1B-7B-0924" and installed < required:
         pytest.skip(
             "MoE models with the Transformers modeling backend require "

tests/v1/e2e/test_spec_decode.py

@@ -455,7 +455,7 @@ def test_eagle_correctness(
         from packaging.version import Version
 
         installed = Version(transformers.__version__)
-        required = Version("5.0.0.dev")
+        required = Version("5.0.0")
         if installed < required:
             pytest.skip(
                 "Eagle3 with the Transformers modeling backend requires "

vllm/model_executor/models/transformers/base.py

@@ -350,7 +350,7 @@ class Base(
         # vLLM does not support encoder-decoder models, so if any encoder layer is
         # found in a text only model, we assume the whole model is an encoder model
         if has_encoder(self.model) and not is_multimodal(self.config):
-            self.check_version("5.0.0.dev0", "encoder models support")
+            self.check_version("5.0.0", "encoder models support")
             attn_type = AttentionType.ENCODER_ONLY
         else:
             attn_type = AttentionType.DECODER
@@ -502,7 +502,7 @@ class Base(
             )
 
     def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
-        self.check_version("5.0.0.dev0", "Eagle3 support")
+        self.check_version("5.0.0", "Eagle3 support")
         from transformers.utils.generic import OutputRecorder
 
         # The default value in PreTrainedModel is None

vllm/model_executor/models/transformers/moe.py

@@ -118,7 +118,7 @@ direct_register_custom_op(
 
 class MoEMixin(MixtureOfExperts):
     def __init__(self, *, vllm_config: "VllmConfig", prefix: str = ""):
-        self.check_version("5.0.0.dev0", "MoE models support")
+        self.check_version("5.0.0", "MoE models support")
         # Skip MixtureOfExperts.__init__ and call the next class in MRO
         super(MixtureOfExperts, self).__init__(vllm_config=vllm_config, prefix=prefix)
 

vllm/transformers_utils/config.py

@@ -328,7 +328,7 @@ def patch_rope_parameters(config: PretrainedConfig) -> None:
     partial_rotary_factor = getattr_iter(config, names, None, warn=True)
     ompe = getattr(config, "original_max_position_embeddings", None)
 
-    if Version(version("transformers")) < Version("5.0.0.dev0"):
+    if Version(version("transformers")) < Version("5.0.0"):
         # Transformers v4 installed, legacy config fields may be present
         if (rope_scaling := getattr(config, "rope_scaling", None)) is not None:
             config.rope_parameters = rope_scaling