Skip to content

vllm.model_executor.models.config

MODELS_CONFIG_MAP module-attribute 

MODELS_CONFIG_MAP: dict[
    str, type[VerifyAndUpdateConfig]
] = {
    "GteModel": SnowflakeGteNewModelConfig,
    "GteNewModel": GteNewModelConfig,
    "NomicBertModel": NomicBertModelConfig,
    "Qwen3ForSequenceClassification": Qwen3ForSequenceClassificationConfig,
    "XLMRobertaModel": JinaRobertaModelConfig,
}

logger module-attribute 

logger = init_logger(__name__)

GteNewModelConfig

Bases: VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
class GteNewModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "NewConfig"
        assert config.hidden_act == "gelu"

        config.hidden_act = "geglu"

        head_dim = config.hidden_size // config.num_attention_heads
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
            "max_position": config.max_position_embeddings,
            "base": config.rope_theta,
            "rope_scaling": getattr(config, "rope_scaling", None)
        }

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    config = vllm_config.model_config.hf_config

    assert config.__class__.__name__ == "NewConfig"
    assert config.hidden_act == "gelu"

    config.hidden_act = "geglu"

    head_dim = config.hidden_size // config.num_attention_heads
    config.rotary_kwargs = {
        "head_size": head_dim,
        "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
        "max_position": config.max_position_embeddings,
        "base": config.rope_theta,
        "rope_scaling": getattr(config, "rope_scaling", None)
    }

JinaRobertaModelConfig

Bases: VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
class JinaRobertaModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        if config.position_embedding_type == "rotary":
            assert config.__class__.__name__ == "XLMRobertaFlashConfig"

            head_dim = config.hidden_size // config.num_attention_heads
            config.rotary_kwargs = {
                "head_size": head_dim,
                "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
                "max_position": config.max_position_embeddings,
                "base": getattr(config, "rope_theta", config.rotary_emb_base),
                "rope_scaling": getattr(config, "rope_scaling", None)
            }

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    config = vllm_config.model_config.hf_config

    if config.position_embedding_type == "rotary":
        assert config.__class__.__name__ == "XLMRobertaFlashConfig"

        head_dim = config.hidden_size // config.num_attention_heads
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
            "max_position": config.max_position_embeddings,
            "base": getattr(config, "rope_theta", config.rotary_emb_base),
            "rope_scaling": getattr(config, "rope_scaling", None)
        }

NomicBertModelConfig

Bases: VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
class NomicBertModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "NomicBertConfig"
        assert config.activation_function in ["swiglu", "gelu"]
        config.position_embedding_type = getattr(config,
                                                 "position_embedding_type",
                                                 "rope")

        if config.activation_function == "swiglu":
            config.hidden_act = "silu"
        else:
            config.hidden_act = config.activation_function

        assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
                config.qkv_proj_bias)
        config.bias = config.qkv_proj_bias

        assert config.rotary_emb_scale_base is None
        assert not config.rotary_emb_interleaved

        config.layer_norm_eps = config.layer_norm_epsilon
        config.intermediate_size = config.n_inner
        config.hidden_size = config.n_embd
        config.num_hidden_layers = config.n_layer

        head_dim = config.hidden_size // config.num_attention_heads
        rotary_emb_dim = head_dim * config.rotary_emb_fraction
        max_trained_positions = getattr(config, "max_trained_positions", 2048)
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": rotary_emb_dim,
            "max_position": max_trained_positions,
            "base": getattr(config, "rope_theta", config.rotary_emb_base),
            "rope_scaling": getattr(config, "rope_scaling", None)
        }

        # we ignore config.rotary_scaling_factor so that for datasets shorter
        # than max_trained_positions 2048, the results are consistent
        # with SentenceTransformer.
        # The context extension uses vllm style rope_theta and rope_scaling.
        # See #17785 #18755
        if (not vllm_config.model_config.hf_overrides
                and vllm_config.model_config.original_max_model_len is None):
            # Default
            # Reset max_model_len to max_trained_positions.
            # nomic-embed-text-v2-moe the length is set to 512
            # by sentence_bert_config.json.
            max_model_len_before = vllm_config.model_config.max_model_len
            max_model_len = min(vllm_config.model_config.max_model_len,
                                max_trained_positions)

            vllm_config.recalculate_max_model_len(max_model_len)
            logger.warning(
                "Nomic context extension is disabled. "
                "Changing max_model_len from %s to %s. "
                "To enable context extension, see: "
                "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
                max_model_len_before, vllm_config.model_config.max_model_len)
        else:
            # We need to re-verify max_model_len to avoid lengths
            # greater than position_embedding.
            model_config = vllm_config.model_config
            hf_text_config = model_config.hf_text_config

            if isinstance(model_config.hf_overrides, dict):
                # hf_overrides_kw
                max_model_len = model_config.hf_overrides.get(
                    "max_model_len", vllm_config.model_config.max_model_len)
            else:
                # hf_overrides_fn
                # This might be overridden by sentence_bert_config.json.
                max_model_len = vllm_config.model_config.max_model_len

            # reset hf_text_config for recalculate_max_model_len.
            if hasattr(hf_text_config, "max_model_len"):
                delattr(hf_text_config, "max_model_len")
            hf_text_config.max_position_embeddings = max_trained_positions
            hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]

            # The priority of sentence_bert_config.json is higher
            # than max_position_embeddings
            encoder_config = deepcopy(model_config.encoder_config)
            encoder_config.pop("max_seq_length", None)
            model_config.encoder_config = encoder_config

            vllm_config.recalculate_max_model_len(max_model_len)

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    config = vllm_config.model_config.hf_config

    assert config.__class__.__name__ == "NomicBertConfig"
    assert config.activation_function in ["swiglu", "gelu"]
    config.position_embedding_type = getattr(config,
                                             "position_embedding_type",
                                             "rope")

    if config.activation_function == "swiglu":
        config.hidden_act = "silu"
    else:
        config.hidden_act = config.activation_function

    assert (config.mlp_fc1_bias == config.mlp_fc2_bias ==
            config.qkv_proj_bias)
    config.bias = config.qkv_proj_bias

    assert config.rotary_emb_scale_base is None
    assert not config.rotary_emb_interleaved

    config.layer_norm_eps = config.layer_norm_epsilon
    config.intermediate_size = config.n_inner
    config.hidden_size = config.n_embd
    config.num_hidden_layers = config.n_layer

    head_dim = config.hidden_size // config.num_attention_heads
    rotary_emb_dim = head_dim * config.rotary_emb_fraction
    max_trained_positions = getattr(config, "max_trained_positions", 2048)
    config.rotary_kwargs = {
        "head_size": head_dim,
        "rotary_dim": rotary_emb_dim,
        "max_position": max_trained_positions,
        "base": getattr(config, "rope_theta", config.rotary_emb_base),
        "rope_scaling": getattr(config, "rope_scaling", None)
    }

    # we ignore config.rotary_scaling_factor so that for datasets shorter
    # than max_trained_positions 2048, the results are consistent
    # with SentenceTransformer.
    # The context extension uses vllm style rope_theta and rope_scaling.
    # See #17785 #18755
    if (not vllm_config.model_config.hf_overrides
            and vllm_config.model_config.original_max_model_len is None):
        # Default
        # Reset max_model_len to max_trained_positions.
        # nomic-embed-text-v2-moe the length is set to 512
        # by sentence_bert_config.json.
        max_model_len_before = vllm_config.model_config.max_model_len
        max_model_len = min(vllm_config.model_config.max_model_len,
                            max_trained_positions)

        vllm_config.recalculate_max_model_len(max_model_len)
        logger.warning(
            "Nomic context extension is disabled. "
            "Changing max_model_len from %s to %s. "
            "To enable context extension, see: "
            "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/context_extension.html",
            max_model_len_before, vllm_config.model_config.max_model_len)
    else:
        # We need to re-verify max_model_len to avoid lengths
        # greater than position_embedding.
        model_config = vllm_config.model_config
        hf_text_config = model_config.hf_text_config

        if isinstance(model_config.hf_overrides, dict):
            # hf_overrides_kw
            max_model_len = model_config.hf_overrides.get(
                "max_model_len", vllm_config.model_config.max_model_len)
        else:
            # hf_overrides_fn
            # This might be overridden by sentence_bert_config.json.
            max_model_len = vllm_config.model_config.max_model_len

        # reset hf_text_config for recalculate_max_model_len.
        if hasattr(hf_text_config, "max_model_len"):
            delattr(hf_text_config, "max_model_len")
        hf_text_config.max_position_embeddings = max_trained_positions
        hf_text_config.rope_scaling = config.rotary_kwargs["rope_scaling"]

        # The priority of sentence_bert_config.json is higher
        # than max_position_embeddings
        encoder_config = deepcopy(model_config.encoder_config)
        encoder_config.pop("max_seq_length", None)
        model_config.encoder_config = encoder_config

        vllm_config.recalculate_max_model_len(max_model_len)

Qwen3ForSequenceClassificationConfig

Bases: VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
class Qwen3ForSequenceClassificationConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        is_original_qwen3_reranker = getattr(config,
                                             "is_original_qwen3_reranker",
                                             False)

        if not is_original_qwen3_reranker:
            return

        tokens = getattr(config, "classifier_from_token", None)
        assert tokens is not None and len(tokens) == 2, \
            ("Try loading the original Qwen3 Reranker?, see: "
             "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py")
        vllm_config.model_config.hf_config.method = "from_2_way_softmax"

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    config = vllm_config.model_config.hf_config

    is_original_qwen3_reranker = getattr(config,
                                         "is_original_qwen3_reranker",
                                         False)

    if not is_original_qwen3_reranker:
        return

    tokens = getattr(config, "classifier_from_token", None)
    assert tokens is not None and len(tokens) == 2, \
        ("Try loading the original Qwen3 Reranker?, see: "
         "https://github.com/vllm-project/vllm/tree/main/examples/offline_inference/qwen3_reranker.py")
    vllm_config.model_config.hf_config.method = "from_2_way_softmax"

SnowflakeGteNewModelConfig

Bases: VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
class SnowflakeGteNewModelConfig(VerifyAndUpdateConfig):

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        config = vllm_config.model_config.hf_config

        assert config.__class__.__name__ == "GteConfig"
        assert config.hidden_act == "gelu"

        config.hidden_act = "geglu"

        head_dim = config.hidden_size // config.num_attention_heads
        config.rotary_kwargs = {
            "head_size": head_dim,
            "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
            "max_position": config.max_position_embeddings,
            "base": config.rope_theta,
            "rope_scaling": getattr(config, "rope_scaling", None)
        }

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    config = vllm_config.model_config.hf_config

    assert config.__class__.__name__ == "GteConfig"
    assert config.hidden_act == "gelu"

    config.hidden_act = "geglu"

    head_dim = config.hidden_size // config.num_attention_heads
    config.rotary_kwargs = {
        "head_size": head_dim,
        "rotary_dim": getattr(config, "rotary_emb_dim", head_dim),
        "max_position": config.max_position_embeddings,
        "base": config.rope_theta,
        "rope_scaling": getattr(config, "rope_scaling", None)
    }

VerifyAndUpdateConfig

Source code in vllm/model_executor/models/config.py 
14
15
16
17
18
class VerifyAndUpdateConfig:

    @staticmethod
    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
        raise NotImplementedError

verify_and_update_config staticmethod 

verify_and_update_config(vllm_config: VllmConfig) -> None
Source code in vllm/model_executor/models/config.py 
16
17
18
@staticmethod
def verify_and_update_config(vllm_config: "VllmConfig") -> None:
    raise NotImplementedError