diff --git a/mteb/models/model2vec_models.py b/mteb/models/model2vec_models.py
index ee79f1cafa..2b0a2ed02c 100644
--- a/mteb/models/model2vec_models.py
+++ b/mteb/models/model2vec_models.py
@@ -235,3 +235,122 @@ def encode(
     public_training_code="https://github.com/MinishLab/model2vec",
     public_training_data=None,
 )
+
+pubmed_bert_100k = ModelMeta(
+    loader=partial(
+        Model2VecWrapper, model_name="NeuML/pubmedbert-base-embeddings-100K"
+    ),
+    name="NeuML/pubmedbert-base-embeddings-100K",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="bac5e3b12fb8c650e92a19c41b436732c4f16e9e",
+    release_date="2025-01-03",
+    n_parameters=1 * 1e5,
+    memory_usage_mb=0,
+    max_tokens=np.inf,
+    embed_dim=64,
+    license="apache-2.0",
+    similarity_fn_name="cosine",
+    framework=["NumPy"],
+    reference="https://huggingface.co/NeuML/pubmedbert-base-embeddings-100K",
+    use_instructions=False,
+    adapted_from="NeuML/pubmedbert-base-embeddings",
+    superseded_by=None,
+    training_datasets={},
+    public_training_code="https://huggingface.co/NeuML/pubmedbert-base-embeddings-100K#training",
+    public_training_data="https://pubmed.ncbi.nlm.nih.gov/download/",
+)
+
+pubmed_bert_500k = ModelMeta(
+    loader=partial(
+        Model2VecWrapper, model_name="NeuML/pubmedbert-base-embeddings-500K"
+    ),
+    name="NeuML/pubmedbert-base-embeddings-500K",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="34ba71e35c393fdad7ed695113f653feb407b16b",
+    release_date="2025-01-03",
+    n_parameters=5 * 1e5,
+    memory_usage_mb=2,
+    max_tokens=np.inf,
+    embed_dim=64,
+    license="apache-2.0",
+    similarity_fn_name="cosine",
+    framework=["NumPy"],
+    reference="https://huggingface.co/NeuML/pubmedbert-base-embeddings-500K",
+    use_instructions=False,
+    adapted_from="NeuML/pubmedbert-base-embeddings",
+    superseded_by=None,
+    training_datasets={},
+    public_training_code="https://huggingface.co/NeuML/pubmedbert-base-embeddings-500K#training",
+    public_training_data="https://pubmed.ncbi.nlm.nih.gov/download/",
+)
+
+pubmed_bert_1m = ModelMeta(
+    loader=partial(Model2VecWrapper, model_name="NeuML/pubmedbert-base-embeddings-1M"),
+    name="NeuML/pubmedbert-base-embeddings-1M",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="2b7fed222594708da6d88bcda92ae9b434b7ddd1",
+    release_date="2025-01-03",
+    n_parameters=1 * 1e6,
+    memory_usage_mb=2,
+    max_tokens=np.inf,
+    embed_dim=64,
+    license="apache-2.0",
+    similarity_fn_name="cosine",
+    framework=["NumPy"],
+    reference="https://huggingface.co/NeuML/pubmedbert-base-embeddings-1M",
+    use_instructions=False,
+    adapted_from="NeuML/pubmedbert-base-embeddings",
+    superseded_by=None,
+    training_datasets={},
+    public_training_code="https://huggingface.co/NeuML/pubmedbert-base-embeddings-1M#training",
+    public_training_data="https://pubmed.ncbi.nlm.nih.gov/download/",
+)
+
+pubmed_bert_2m = ModelMeta(
+    loader=partial(Model2VecWrapper, model_name="NeuML/pubmedbert-base-embeddings-2M"),
+    name="NeuML/pubmedbert-base-embeddings-2M",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="1d7bbe04d6713e425161146bfdc71473cbed498a",
+    release_date="2025-01-03",
+    n_parameters=1.95 * 1e6,
+    memory_usage_mb=7,
+    max_tokens=np.inf,
+    embed_dim=64,
+    license="apache-2.0",
+    similarity_fn_name="cosine",
+    framework=["NumPy"],
+    reference="https://huggingface.co/NeuML/pubmedbert-base-embeddings-2M",
+    use_instructions=False,
+    adapted_from="NeuML/pubmedbert-base-embeddings",
+    superseded_by=None,
+    training_datasets={},
+    public_training_code="https://huggingface.co/NeuML/pubmedbert-base-embeddings-2M#training",
+    public_training_data="https://pubmed.ncbi.nlm.nih.gov/download/",
+)
+
+pubmed_bert_8m = ModelMeta(
+    loader=partial(Model2VecWrapper, model_name="NeuML/pubmedbert-base-embeddings-8M"),
+    name="NeuML/pubmedbert-base-embeddings-8M",
+    languages=["eng_Latn"],
+    open_weights=True,
+    revision="387d350015e963744f4fafe56a574b7cd48646c9",
+    release_date="2025-01-03",
+    n_parameters=7.81 * 1e6,
+    memory_usage_mb=30,
+    max_tokens=np.inf,
+    embed_dim=256,
+    license="apache-2.0",
+    similarity_fn_name="cosine",
+    framework=["NumPy"],
+    reference="https://huggingface.co/NeuML/pubmedbert-base-embeddings-8M",
+    use_instructions=False,
+    adapted_from="NeuML/pubmedbert-base-embeddings",
+    superseded_by=None,
+    training_datasets={},
+    public_training_code="https://huggingface.co/NeuML/pubmedbert-base-embeddings-8M#training",
+    public_training_data="https://pubmed.ncbi.nlm.nih.gov/download/",
+)