[model] Support MiniCPM-V 4.5 (#23586)

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docs/models/supported_models.md

@@ -638,7 +638,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
 | `LlavaNextVideoForConditionalGeneration` | LLaVA-NeXT-Video | T + V | `llava-hf/LLaVA-NeXT-Video-7B-hf`, etc. | | ✅︎ | ✅︎ |
 | `LlavaOnevisionForConditionalGeneration` | LLaVA-Onevision | T + I<sup>+</sup> + V<sup>+</sup> | `llava-hf/llava-onevision-qwen2-7b-ov-hf`, `llava-hf/llava-onevision-qwen2-0.5b-ov-hf`, etc. | | ✅︎ | ✅︎ |
 | `MiniCPMO` | MiniCPM-O | T + I<sup>E+</sup> + V<sup>E+</sup> + A<sup>E+</sup> | `openbmb/MiniCPM-o-2_6`, etc. | ✅︎ | ✅︎ | ✅︎ |
-| `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, etc. | ✅︎ | | ✅︎ |
+| `MiniCPMV` | MiniCPM-V | T + I<sup>E+</sup> + V<sup>E+</sup> | `openbmb/MiniCPM-V-2` (see note), `openbmb/MiniCPM-Llama3-V-2_5`, `openbmb/MiniCPM-V-2_6`, `openbmb/MiniCPM-V-4`, `openbmb/MiniCPM-V-4_5`, etc. | ✅︎ | | ✅︎ |
 | `MiniMaxVL01ForConditionalGeneration` | MiniMax-VL | T + I<sup>E+</sup> | `MiniMaxAI/MiniMax-VL-01`, etc. | | ✅︎ | ✅︎ |
 | `Mistral3ForConditionalGeneration` | Mistral3 (HF Transformers) | T + I<sup>+</sup> | `mistralai/Mistral-Small-3.1-24B-Instruct-2503`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `MllamaForConditionalGeneration` | Llama 3.2 | T + I<sup>+</sup> | `meta-llama/Llama-3.2-90B-Vision-Instruct`, `meta-llama/Llama-3.2-11B-Vision`, etc. | | | |

tests/models/registry.py

@@ -451,7 +451,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "MiniCPMO": _HfExamplesInfo("openbmb/MiniCPM-o-2_6",
                                 trust_remote_code=True),
     "MiniCPMV": _HfExamplesInfo("openbmb/MiniCPM-Llama3-V-2_5",
-                                extras={"2.6": "openbmb/MiniCPM-V-2_6", "4.0": "openbmb/MiniCPM-V-4"},  # noqa: E501
+                                extras={"2.6": "openbmb/MiniCPM-V-2_6", "4.0": "openbmb/MiniCPM-V-4", "4.5": "openbmb/MiniCPM-V-4_5"},  # noqa: E501
                                 trust_remote_code=True),
     "MiniMaxVL01ForConditionalGeneration": _HfExamplesInfo("MiniMaxAI/MiniMax-VL-01", # noqa: E501
                                               trust_remote_code=True,

vllm/model_executor/models/minicpmv.py

@@ -27,12 +27,14 @@ import math
 from collections import defaultdict
 from collections.abc import Iterable, Mapping, Sequence
 from functools import partial
+from itertools import chain
 from typing import Annotated, Any, Callable, Literal, Optional, Union
 
 import numpy as np
 import torch
 import torch.types
 from torch import nn
+from torch.nn.init import trunc_normal_
 from transformers import BatchFeature, PretrainedConfig
 from typing_extensions import TypeVar
 
@@ -47,10 +49,11 @@ from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.minicpm import MiniCPMForCausalLM
 from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM
+from vllm.model_executor.models.qwen3 import Qwen3ForCausalLM
 from vllm.model_executor.sampling_metadata import SamplingMetadata
-from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
+from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
-                                    NestedTensors)
+                                    MultiModalKwargsItems, NestedTensors)
 from vllm.multimodal.parse import (DictEmbeddingItems, ImageItem,
                                    ImageProcessorItems, ImageSize,
                                    ModalityData, ModalityDataItems,
@@ -218,6 +221,187 @@ class Resampler2_5(BaseResampler):
         return x
 
 
+class Resampler4_5(Resampler2_5):
+
+    def __init__(self,
+                 num_queries: int,
+                 embed_dim: int,
+                 num_heads: int,
+                 kv_dim: Optional[int] = None,
+                 norm_layer: Callable[[int], nn.LayerNorm] = DEFAULT_LN,
+                 max_size: tuple[int, int] = (70, 70),
+                 max_temporal_size: int = 36000,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 prefix: str = "") -> None:
+        super().__init__(num_queries,
+                         embed_dim,
+                         num_heads,
+                         kv_dim,
+                         norm_layer,
+                         max_size,
+                         quant_config=quant_config,
+                         prefix=prefix)
+
+        trunc_normal_(self.query, std=.02)
+        self.max_temporal_size = max_temporal_size
+        self._set_temporal_pos_cache(self.max_temporal_size)
+        self.apply(self._init_weights)
+
+    def get_1d_sincos_pos_embed_from_temporal_size(self, embed_dim: int,
+                                                   pos: np.ndarray):
+        """
+        embed_dim: output dimension for each position
+        pos: a list of positions to be encoded: size (M,)
+        out: (M, D)
+        """
+        assert embed_dim % 2 == 0
+        omega = np.arange(embed_dim // 2, dtype=np.float32)
+        omega /= embed_dim / 2.
+        omega = 1. / 10000**omega  # (D/2,)
+
+        pos = pos.reshape(-1)  # (M,)
+        out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+        emb_sin = np.sin(out)  # (M, D/2)
+        emb_cos = np.cos(out)  # (M, D/2)
+
+        emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+        return emb
+
+    def _set_temporal_pos_cache(self,
+                                max_temporal_size: int,
+                                device: torch.types.Device = "cpu") -> None:
+        temporal_size = np.arange(max_temporal_size, dtype=np.float32)
+        pos_embed = torch.from_numpy(
+            self.get_1d_sincos_pos_embed_from_temporal_size(
+                self.embed_dim, temporal_size)).float().to(device)
+        self.register_buffer("temporal_pos_embed", pos_embed, persistent=False)
+
+    def _adjust_temporal_pos_cache(self,
+                                   max_temporal_size: int,
+                                   device: torch.types.Device = "cpu"):
+        if max_temporal_size > self.max_temporal_size:
+            self.max_temporal_size = max_temporal_size
+            self._set_temporal_pos_cache(self.max_temporal_size, device)
+
+    def _init_weights(self, m: Union[nn.Linear, nn.LayerNorm]):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        tgt_sizes: torch.Tensor,
+        # temporal_ids for high refresh rate videos
+        temporal_ids=None
+    ) -> torch.Tensor:
+        assert x.shape[0] == tgt_sizes.shape[0]
+        bs = x.shape[0]
+
+        device = x.device
+        dtype = x.dtype
+
+        patch_len = tgt_sizes[:, 0] * tgt_sizes[:, 1]
+
+        self._adjust_pos_cache(tgt_sizes, device=device)
+
+        temporal_pos_emb = False
+        temporal_ids_flatten = None
+        if temporal_ids is not None:
+            # example: [[-1], [-1], [2, 6, 9]]
+            temporal_ids_flatten = list(chain.from_iterable(temporal_ids))
+            max_temporal_size = max(temporal_ids_flatten, default=0)
+            if max_temporal_size > -1:
+                temporal_pos_emb = True
+            if max_temporal_size > self.max_temporal_size:
+                self._adjust_temporal_pos_cache(max_temporal_size, device)
+
+        max_patch_len = patch_len.max().item()
+        assert isinstance(max_patch_len, int)
+
+        key_padding_mask = torch.zeros((bs, max_patch_len),
+                                       dtype=torch.bool,
+                                       device=device)
+
+        x, _ = self.kv_proj(x)  # B * L * D
+        x = self.ln_kv(x).permute(1, 0, 2)  # L * B * D
+        q = self.ln_q(self.query)  # Q * D
+
+        pos_embed_2d = []
+        pos_embed_temporal = []
+        for i in range(bs):
+            tgt_h, tgt_w = tgt_sizes[i]
+            if temporal_pos_emb:
+                if temporal_ids_flatten[i] == -1:
+                    pos_embed_temporal.append(
+                        torch.zeros(self.embed_dim, dtype=dtype,
+                                    device=device))
+                else:
+                    pos_embed_temporal.append(self.temporal_pos_embed[
+                        temporal_ids_flatten[i]].to(dtype))  # D
+
+            pos_embed_2d.append(self.pos_embed[:tgt_h, :tgt_w, :].reshape(
+                (tgt_h * tgt_w, -1)).to(dtype))  # patches * D
+            key_padding_mask[i, patch_len[i]:] = True
+
+        pos_embed_2d = torch.nn.utils.rnn.pad_sequence(
+            pos_embed_2d, batch_first=True,
+            padding_value=0.0).permute(1, 0, 2)  # BLD => L * B * D
+
+        k = x
+        v = x + pos_embed_2d
+        if pos_embed_temporal:
+            k += torch.stack(pos_embed_temporal, dim=0)
+            bs = len(temporal_ids)
+            merge_k = []
+            merge_v = []
+            merge_key_padding_mask = []
+
+            start = 0
+            for tp in temporal_ids:
+                end = start + len(tp)
+                # L * (end-start) * D -> (end-start) * L * D
+                # -> 1 * L*(end-start) * D
+                merge_k.append(k[:, start:end, :].permute(1, 0, 2).reshape(
+                    -1, self.embed_dim))
+                merge_v.append(v[:, start:end, :].permute(1, 0, 2).reshape(
+                    -1, self.embed_dim))
+                merge_key_padding_mask.append(
+                    key_padding_mask[start:end, :].reshape(-1, 1))
+
+                start = end
+
+            k = torch.nn.utils.rnn.pad_sequence(merge_k,
+                                                batch_first=True,
+                                                padding_value=0.0).permute(
+                                                    1, 0, 2)  # L*(end-start)
+            v = torch.nn.utils.rnn.pad_sequence(merge_v,
+                                                batch_first=True,
+                                                padding_value=0.0).permute(
+                                                    1, 0, 2)  # L*(end-start)
+            key_padding_mask = torch.nn.utils.rnn.pad_sequence(
+                merge_key_padding_mask, batch_first=True,
+                padding_value=True).squeeze(-1)
+
+        out = self.attn(
+            self._repeat(q, bs),  # Q * B * D
+            k,  # L * B * D +  L * B * D
+            v,
+            key_padding_mask=key_padding_mask,
+        )[0]
+        #  out: Q * B * D
+        x = out.permute(1, 0, 2)  # B * Q * D
+
+        x = self.ln_post(x)
+        x = x @ self.proj
+        return x
+
+
 def get_version_by_config(config: PretrainedConfig) -> tuple[int, ...]:
     version_float = getattr(config, "version", None)
 
@@ -354,9 +538,7 @@ class MiniCPMVProcessingInfo(BaseProcessingInfo):
 
     def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
         mm_limits = {"image": None}
-        if self.get_model_version() == (2,
+        if self.get_model_version() in {(2, 6), (4, 0), (4, 5)}:
-                                        6) or self.get_model_version() == (4,
-                                                                           0):
             mm_limits["video"] = None
 
         return mm_limits
@@ -637,8 +819,7 @@ class MiniCPMVMultiModalProcessor(BaseMultiModalProcessor[_I]):
         out_keys: set[str],
     ) -> dict[str, NestedTensors]:
         # This processor supports zipping prompt and mm_data together
-        if self.info.get_model_version() == (
+        if self.info.get_model_version() in {(2, 6), (4, 0), (4, 5)}:
-                2, 6) or self.info.get_model_version() == (4, 0):
             inputs = super()._call_hf_processor(
                 prompt=prompts,  # type: ignore
                 mm_data=mm_data,
@@ -816,7 +997,6 @@ class MiniCPMVBaseModel(nn.Module, SupportsMultiModal, SupportsPP):
         # and config class
         self.config = config
         self.multimodal_config = multimodal_config
-        self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
 
         self.version = get_version_by_config(self.config)
         self.llm = self.init_llm(vllm_config=vllm_config,
@@ -1364,11 +1544,9 @@ class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
         prefix: str = "",
     ) -> nn.Module:
         quant_config = self._maybe_ignore_quant_config(quant_config)
-        model = Idefics2VisionTransformer(
+        model = Idefics2VisionTransformer(config.vision_config,
-            config.vision_config,
+                                          quant_config=quant_config,
-            quant_config=quant_config,
+                                          prefix=prefix)
-            prefix=prefix,
-            use_data_parallel=self.use_data_parallel)
         if self.config.drop_vision_last_layer:
             model.encoder.layers = model.encoder.layers[:-1]
         return model
@@ -1436,11 +1614,121 @@ class MiniCPMV4_0(MiniCPMVBaseModel, SupportsLoRA):
         return loader.load_weights(weights)
 
 
+class MiniCPMV4_5(MiniCPMVBaseModel, SupportsLoRA):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__(vllm_config=vllm_config, prefix=prefix)
+        assert self.version == (4, 5)
+
+    def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig):
+        if isinstance(quant_config, (AWQConfig, AWQMarlinConfig)):
+            return None
+        return quant_config
+
+    def init_llm(
+        self,
+        vllm_config: VllmConfig,
+        prefix: str = "",
+    ) -> nn.Module:
+        return Qwen3ForCausalLM(vllm_config=vllm_config, prefix=prefix)
+
+    def init_vision_module(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        model = Idefics2VisionTransformer(config.vision_config,
+                                          quant_config=quant_config,
+                                          prefix=prefix)
+        if self.config.drop_vision_last_layer:
+            model.encoder.layers = model.encoder.layers[:-1]
+        return model
+
+    def init_resampler(
+        self,
+        embed_dim: int,
+        vision_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> nn.Module:
+        quant_config = self._maybe_ignore_quant_config(quant_config)
+        with set_default_torch_dtype(torch.float16):
+            # The resampler in 4.0 remains consistent with the one in 2.5/2.6.
+            resampler = Resampler4_5(num_queries=self.config.query_num,
+                                     embed_dim=embed_dim,
+                                     num_heads=embed_dim // 128,
+                                     kv_dim=vision_dim,
+                                     quant_config=quant_config,
+                                     prefix=prefix)
+
+        return resampler.to(device=current_platform.device_type,
+                            dtype=torch.get_default_dtype())
+
+    def get_vision_hidden_states(
+            self, data: MiniCPMVImagePixelInputs) -> torch.Tensor:
+        pixel_values = data["pixel_values"]
+        tgt_sizes = data["tgt_sizes"]
+        temporal_ids = data.get('temporal_ids', None)
+
+        B = len(pixel_values)
+        P = pixel_values[0].shape[-2]
+        L = max(item.shape[-1] for item in pixel_values)
+        device = pixel_values[0].device
+        dtype = pixel_values[0].dtype
+
+        all_pixel_values = torch.zeros((B, 3, P, L),
+                                       dtype=dtype,
+                                       device=device)
+        all_temporal_ids = None if temporal_ids is None else flatten_2d_lists(
+            temporal_ids)
+        for i, pixel_values_item in enumerate(pixel_values):
+            L_item = pixel_values_item.shape[-1]
+            all_pixel_values[i, ..., :L_item] = pixel_values_item
+
+        num_patches = tgt_sizes.prod(-1)
+        max_patches = num_patches.max().item()
+        assert isinstance(max_patches, int)
+
+        patch_attn_mask = torch.zeros((B, max_patches),
+                                      dtype=torch.bool,
+                                      device=device)
+        for i, num_patches_item in enumerate(num_patches):
+            patch_attn_mask[i, :num_patches_item] = True
+
+        vision_embedding = self.vpm(
+            all_pixel_values,
+            patch_attention_mask=patch_attn_mask.unsqueeze(1),
+            tgt_sizes=tgt_sizes,
+        )
+
+        return self.resampler(vision_embedding, tgt_sizes, all_temporal_ids)
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self,
+                                   skip_prefixes=["apm.", "audio", "tts"])
+        return loader.load_weights(weights)
+
+
 _SUPPORT_VERSION = {
     (2, 0): MiniCPMV2_0,
     (2, 5): MiniCPMV2_5,
     (2, 6): MiniCPMV2_6,
     (4, 0): MiniCPMV4_0,
+    (4, 5): MiniCPMV4_5,
 }
 
 

vllm/transformers_utils/chat_templates/registry.py

@@ -20,6 +20,16 @@ def _get_qwen_chat_template_fallback(
     return CHAT_TEMPLATES_DIR / "template_basic.jinja"
 
 
+def _get_minicpmv_chat_template_fallback(
+        tokenizer_name_or_path: str) -> Optional[Path]:
+    # MiniCPM-V-4.5 version uses a dedicated template
+    if "4.5" in tokenizer_name_or_path or "4_5" in tokenizer_name_or_path:
+        return CHAT_TEMPLATES_DIR / "template_minicpmv45.jinja"
+
+    # Other versions use chatml template
+    return CHAT_TEMPLATES_DIR / "template_chatml.jinja"
+
+
 # yapf: disable
 _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK: dict[str, ChatTemplatePath] = {
     "blip-2": CHAT_TEMPLATES_DIR / "template_blip2.jinja",
@@ -27,6 +37,7 @@ _MODEL_TYPE_TO_CHAT_TEMPLATE_FALLBACK: dict[str, ChatTemplatePath] = {
     "deepseek_vl_v2": CHAT_TEMPLATES_DIR / "template_deepseek_vl2.jinja",
     "florence2": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "fuyu": CHAT_TEMPLATES_DIR / "template_fuyu.jinja",
+    "minicpmv": _get_minicpmv_chat_template_fallback,
     "paligemma": CHAT_TEMPLATES_DIR / "template_basic.jinja",
     "qwen": _get_qwen_chat_template_fallback,
 }

vllm/transformers_utils/chat_templates/template_minicpmv45.jinja

@@ -0,0 +1,93 @@
+{%- set enable_thinking = enable_thinking | default(false) %}
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
+                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+    {%- if enable_thinking is defined and enable_thinking is true %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}