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

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-01-27 02:53:22 +08:00
committed by GitHub
parent 19ab0f7ce5
commit c25dbee40d
6 changed files with 20 additions and 20 deletions
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28
tests/models/registry.py
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@@ -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(

2
tests/models/test_transformers.py
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@@ -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 "

2
tests/v1/e2e/test_spec_decode.py
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@@ -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 "

4
vllm/model_executor/models/transformers/base.py
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@@ -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

2
vllm/model_executor/models/transformers/moe.py
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@@ -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)

2
vllm/transformers_utils/config.py
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@@ -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