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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

config.json

@@ -0,0 +1,174 @@
+{
+    "_commit_hash": null,
+    "architectures": [
+        "POINTSV15ChatModel"
+    ],
+    "auto_map": {
+        "AutoConfig": "configuration_pointsv15_chat.POINTSV15ChatConfig",
+        "AutoModelForCausalLM": "modeling_pointsv15_chat.POINTSV15ChatModel"
+    },
+    "llm_config": {
+        "_attn_implementation_autoset": true,
+        "_name_or_path": "",
+        "add_cross_attention": false,
+        "architectures": null,
+        "attention_bias": false,
+        "attention_dropout": 0.0,
+        "bad_words_ids": null,
+        "begin_suppress_tokens": null,
+        "bos_token_id": null,
+        "chunk_size_feed_forward": 0,
+        "cross_attention_hidden_size": null,
+        "decoder_start_token_id": null,
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": false,
+        "encoder_no_repeat_ngram_size": 0,
+        "eos_token_id": null,
+        "exponential_decay_length_penalty": null,
+        "finetuning_task": null,
+        "forced_bos_token_id": null,
+        "forced_eos_token_id": null,
+        "head_dim": 128,
+        "hidden_act": "silu",
+        "hidden_size": 4096,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "initializer_range": 0.02,
+        "intermediate_size": 12288,
+        "is_decoder": false,
+        "is_encoder_decoder": false,
+        "label2id": {
+            "LABEL_0": 0,
+            "LABEL_1": 1
+        },
+        "length_penalty": 1.0,
+        "max_length": 20,
+        "max_position_embeddings": 32768,
+        "max_window_layers": 28,
+        "min_length": 0,
+        "model_type": "qwen3",
+        "no_repeat_ngram_size": 0,
+        "num_attention_heads": 32,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_hidden_layers": 36,
+        "num_key_value_heads": 8,
+        "num_return_sequences": 1,
+        "output_attentions": false,
+        "output_hidden_states": false,
+        "output_scores": false,
+        "pad_token_id": null,
+        "prefix": null,
+        "problem_type": null,
+        "pruned_heads": {},
+        "remove_invalid_values": false,
+        "repetition_penalty": 1.0,
+        "return_dict": true,
+        "return_dict_in_generate": false,
+        "rms_norm_eps": 1e-06,
+        "rope_scaling": null,
+        "rope_theta": 1000000.0,
+        "sep_token_id": null,
+        "sliding_window": 4096,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": false,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
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+        "transformers_version": "4.51.0",
+        "typical_p": 1.0,
+        "use_bfloat16": false,
+        "use_cache": true,
+        "use_sliding_window": false,
+        "vocab_size": 151936
+    },
+    "torch_dtype": "bfloat16",
+    "transformers_version": null,
+    "vision_config": {
+        "_attn_implementation_autoset": false,
+        "_name_or_path": "",
+        "add_cross_attention": false,
+        "architectures": null,
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+        "chunk_size_feed_forward": 0,
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+        "decoder_start_token_id": null,
+        "depth": 32,
+        "diversity_penalty": 0.0,
+        "do_sample": false,
+        "early_stopping": false,
+        "embed_dim": 1280,
+        "encoder_no_repeat_ngram_size": 0,
+        "eos_token_id": null,
+        "exponential_decay_length_penalty": null,
+        "finetuning_task": null,
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+        "forced_eos_token_id": null,
+        "hidden_act": "quick_gelu",
+        "hidden_size": 3584,
+        "id2label": {
+            "0": "LABEL_0",
+            "1": "LABEL_1"
+        },
+        "in_channels": 3,
+        "in_chans": 3,
+        "is_decoder": false,
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+        "max_length": 20,
+        "min_length": 0,
+        "mlp_ratio": 4,
+        "model_type": "qwen2_vl",
+        "no_repeat_ngram_size": 0,
+        "num_beam_groups": 1,
+        "num_beams": 1,
+        "num_heads": 16,
+        "num_return_sequences": 1,
+        "output_attentions": false,
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+        "output_scores": false,
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+        "patch_size": 14,
+        "prefix": null,
+        "problem_type": null,
+        "pruned_heads": {},
+        "remove_invalid_values": false,
+        "repetition_penalty": 1.0,
+        "return_dict": true,
+        "return_dict_in_generate": false,
+        "sep_token_id": null,
+        "spatial_merge_size": 2,
+        "spatial_patch_size": 14,
+        "suppress_tokens": null,
+        "task_specific_params": null,
+        "temperature": 1.0,
+        "temporal_patch_size": 2,
+        "tf_legacy_loss": false,
+        "tie_encoder_decoder": false,
+        "tie_word_embeddings": true,
+        "tokenizer_class": null,
+        "top_k": 50,
+        "top_p": 1.0,
+        "torch_dtype": "bfloat16",
+        "torchscript": false,
+        "transformers_version": "4.51.0",
+        "typical_p": 1.0,
+        "use_bfloat16": false
+    },
+    "image_token_id": 151655
+}

configuration_pointsv15_chat.py

@@ -0,0 +1,35 @@
+import copy
+from typing import Any, Dict
+
+from transformers import PretrainedConfig, Qwen3Config
+
+try:
+    from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLVisionConfig
+except ImportError:
+    print('Please upgrade transformers to version 4.46.3 or higher')
+
+
+class POINTSV15ChatConfig(PretrainedConfig):
+    model_type = "pointsv1.5_chat"
+    is_composition = True
+    """Configuration class for `POINTSV1.5`."""
+
+    def __init__(self,
+                 **kwargs) -> None:
+        super().__init__(**kwargs)
+        vision_config = kwargs.pop("vision_config", None)
+        llm_config = kwargs.pop("llm_config", None)
+        if isinstance(vision_config, dict):
+            self.vision_config = Qwen2VLVisionConfig(**vision_config)
+        else:
+            self.vision_config = vision_config
+        if isinstance(llm_config, dict):
+            self.llm_config = Qwen3Config(**llm_config)
+        else:
+            self.llm_config = llm_config
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = copy.deepcopy(self.__dict__)
+        output["vision_config"] = self.vision_config.to_dict()
+        output["llm_config"] = self.llm_config.to_dict()
+        return output

generation_config.json

@@ -0,0 +1,4 @@
+{
+  "_from_model_config": true,
+  "transformers_version": "4.51.0"
+}

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+  }
+}

modeling_pointsv15_chat.py

@@ -0,0 +1,243 @@
+import math
+from typing import List, Optional, Tuple
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import (
+    GenerationMixin,
+    PreTrainedModel,
+    PreTrainedTokenizer,
+    Qwen2ForCausalLM,
+)
+
+try:
+    from transformers import Qwen3ForCausalLM
+except ImportError:
+    print('Please upgrade transformers to version 4.51.0 or higher')
+
+try:
+    from transformers.models.qwen2_vl.image_processing_qwen2_vl import (  # noqa
+        Qwen2VLImageProcessor,
+    )
+    from transformers.models.qwen2_vl.modeling_qwen2_vl import PatchMerger
+except ImportError:
+    print('Please upgrade transformers to version 4.46.3 or higher')
+
+from .configuration_pointsv15_chat import POINTSV15ChatConfig
+
+try:
+    from wepoints.models import Qwen2VisionTransformerForNavitPOINTS
+except ImportError:
+    print('Please install WePOINTS, and refer to https://github.com/WePOINTS/WePOINTS')
+
+
+class POINTSV15ChatModel(PreTrainedModel, GenerationMixin):
+    config_class = POINTSV15ChatConfig
+    _no_split_modules = ["CustomLlamaLayer",
+                         "Qwen2VisionTransformerPretrainedModel"]
+
+    """Chat model for POINTSv1.5.
+    
+    Args:
+        config (POINTSChatConfigV15): The model config.
+    """
+
+    def __init__(self, config: POINTSV15ChatConfig, is_distill: int = 0, is_qwen3: int = 1) -> None:
+        super().__init__(config)
+        config.llm_config._attn_implementation = "flash_attention_2"
+        config._attn_implementation_autoset = False
+        if is_qwen3 != 0:
+            self.llm = Qwen3ForCausalLM(config.llm_config)
+        else:
+            self.llm = Qwen2ForCausalLM(config.llm_config)
+        if is_distill != 0:
+            config.vision_config.append_token_num = is_distill
+        self.vision_encoder = Qwen2VisionTransformerForNavitPOINTS._from_config(  # noqa
+            config.vision_config, attn_implementation="flash_attention_2"
+        )
+        self.vision_projector = PatchMerger(config.llm_config.hidden_size,
+                                            context_dim=1280).to(torch.bfloat16)
+        if is_distill != 0:
+            self.vision_projector_distill = PatchMerger(config.llm_config.hidden_size,
+                                                context_dim=1280).to(torch.bfloat16)
+    def process_images(self, images: torch.Tensor, 
+                       image_grid_thws: List[list]) -> torch.Tensor:
+        """Obtain image features from the vision encoder.
+        
+        Args:
+            images (torch.Tensor): The input images.
+            image_grid_thws (List[list]): The grid thresholds for the images.
+
+        Returns:
+            torch.Tensor: The image features.
+        """
+        image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
+        image_features = self.vision_projector(image_features)
+        return image_features
+    
+    def construct_prompt(self, messages: List[dict],
+                         image_processor: Qwen2VLImageProcessor) -> Tuple[str, List[Image.Image], List[list]]: # noqa
+        """Construct the prompt for the chat model.
+
+        Args:
+            messages (List[dict]): The input messages.
+        
+        Returns:
+            Tuple[str, List[Image.Image], List[list]]: 
+                The prompt, images, and image grid shape.
+        """
+        images = []
+        image_grid_thws = []
+        reconstructed_messages = []
+        for message in messages:
+            role = message['role']
+            content_from_role = ''
+            for item in message['content']:
+                if item['type'] == 'text':
+                    content_from_role += item['text']
+                elif item['type'] == 'image':
+                    image_path = item['image']
+                    max_pixels = item['max_pixels'] if 'max_pixels' in item else None
+                    image = Image.open(image_path).convert('RGB')
+                    if max_pixels is not None:
+                        # obtain image size
+                        width, height = image.size
+                        cur_image_pixels = width * height
+                        if cur_image_pixels > max_pixels:
+                            beta = math.sqrt((height * width) / max_pixels)
+                            new_width = math.floor(width / beta)
+                            new_height = math.floor(height / beta)
+                            image = image.resize((new_width, new_height))
+                    image_data = image_processor(images=image)
+                    pixel_values = image_data['pixel_values']
+                    image_grid_thw = image_data['image_grid_thw']
+                    images.extend(pixel_values)
+                    image_grid_thws.append(image_grid_thw)
+                    seq_len = int(image_grid_thw[0][1] * image_grid_thw[0][2] / 4) # noqa
+                    content_from_role += '<|vision_start|>' + '<|image_pad|>' * seq_len + '<|vision_end|>' + '\n' # noqa
+            reconstructed_messages.append({
+                'role': role,
+                'content': content_from_role
+            })
+        prompt = self.apply_chat_template(reconstructed_messages)
+        return prompt, images, image_grid_thws
+    
+    def apply_chat_template(self, messages: List[dict]) -> str:
+        """Apply the chat template to the input messages.
+
+        Args:
+            messages (List[dict]): The input messages.
+        
+        Returns:
+            str: The prompt.
+        """
+        role_prefix_mapping = {
+            'user': '<|im_start|>user\n',
+            'assistant': '<|im_start|>assistant\n'
+        }
+        role = 'user'
+        prompt = ''
+        for message in messages:
+            role = message['role']
+            content = message['content']
+            prompt += role_prefix_mapping[role] + content + '<|im_end|>\n'
+        if role == 'user':
+            prompt += '<|im_start|>assistant\n'
+        return prompt
+
+    @torch.no_grad()
+    def chat(self, 
+             messages: List[dict],
+             tokenizer: PreTrainedTokenizer,
+             image_processor: object,
+             generation_config: dict = None) -> str:
+        """Generate a response to the input prompt.
+
+        Args:
+            messages (List[dict]): The input messages.
+            tokenizer (PreTrainedTokenizer): The tokenizer to use.
+            image_processor (object): The image processor to use.
+            generation_config (dict, optional): The generation config. 
+                Defaults to None.
+        Returns:
+            str: The generated response.
+        """
+        prompt, images, image_grid_thws = self.construct_prompt(
+            messages, image_processor
+        )
+        images = np.array(images)
+        images = torch.from_numpy(images).to(self.vision_encoder.device).to(self.vision_encoder.dtype) # noqa
+        image_grid_thws = np.concatenate(image_grid_thws, axis=0)
+        image_grid_thws = (
+            torch.from_numpy(image_grid_thws)
+            .cuda()
+            .long()
+        )
+        image_features = self.vision_encoder(images, grid_thw=image_grid_thws)
+        
+        image_features = self.vision_projector(image_features)
+        model_inputs = tokenizer(prompt, return_tensors='pt')
+        input_ids = model_inputs['input_ids'].to(self.device)
+        attention_mask = model_inputs['attention_mask'].to(self.device)
+        # stop token
+        eos_token_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
+        # image token
+        image_token_id = tokenizer.convert_tokens_to_ids("<|image_pad|>")
+        generation_config.update(
+            {
+                'eos_token_id': eos_token_id,
+            }
+        )
+        outputs = self.generate(
+            input_ids=input_ids,
+            image_grid_thws=image_grid_thws,
+            attention_mask=attention_mask,
+            image_features=[image_features],
+            image_token_id=image_token_id,
+            **generation_config
+        )
+        response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+        return response
+      
+    def _split_input_ids(self, input_ids, special_token):
+        special_pos = input_ids == special_token
+        pos = (special_pos[:-1] != special_pos[1:]).nonzero() + 1
+        if pos.shape[0] % 2 != 0:
+            pos = torch.cat([torch.tensor([[0]]).to(pos.device), pos])
+        pos = pos.reshape(-1, 2).tolist()
+        return pos
+
+    def generate(self,
+                 input_ids: torch.LongTensor,
+                 image_grid_thws: torch.LongTensor,
+                 attention_mask: torch.LongTensor,
+                 image_features: List[torch.Tensor],
+                 image_token_id: int,
+                 generation_config: Optional[dict] = None,
+                 output_hidden_states: Optional[bool] = None,
+                 **generate_kwargs) -> torch.LongTensor:
+        input_embeddings = self.llm.model.embed_tokens(input_ids)
+        batch_size = input_ids.shape[0]
+        assert len(image_features) == batch_size
+        for i in range(batch_size):
+            pos = self._split_input_ids(input_ids[i], image_token_id)
+            assert len(pos) == len(image_grid_thws)
+            image_pos = [
+                int(image_grid_thw[1] * image_grid_thw[2] / 4)
+                for image_grid_thw in image_grid_thws
+            ]
+            image_pos.insert(0, 0)
+            image_pos = np.cumsum(image_pos)
+            for j, (start, end) in enumerate(pos):
+                input_embeddings[i, start:end] = \
+                    image_features[i][image_pos[j]:image_pos[j+1]]
+        outputs = self.llm.generate(
+            inputs_embeds=input_embeddings,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            output_hidden_states=output_hidden_states,
+            use_cache=True,
+            **generate_kwargs
+        )
+        return outputs

preprocessor_config.json

@@ -0,0 +1,31 @@
+{
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "image_mean": [
+        0.48145466,
+        0.4578275,
+        0.40821073
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+        0.26862954,
+        0.26130258,
+        0.27577711
+    ],
+    "max_pixels": 12845056,
+    "merge_size": 2,
+    "min_pixels": 3136,
+    "patch_size": 14,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "size": {
+        "max_pixels": 12845056,
+        "min_pixels": 3136,
+        "longest_edge": 12845056,
+        "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2,
+    "processor_class": "Qwen2VLProcessor"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

@@ -0,0 +1,239 @@
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+  "add_prefix_space": false,
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+      "content": "<|endoftext|>",
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+      "normalized": false,
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+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "normalized": false,
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+      "single_word": false,
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+      "content": "<|box_start|>",
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "<|box_end|>",
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+      "rstrip": false,
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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}