Upload 16 files

.gitattributes

@@ -0,0 +1,5 @@
+model-00001-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00002-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00003-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00004-of-00004.safetensors filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Qwen3 Coder a3b 480b DISTILL LM STUDIO TOOL USE.preset.json

@@ -0,0 +1,61 @@
+{
+  "identifier": "@local:qwen3-coder-a3b-480b-distill-lm-studio-tool-use",
+  "name": "Qwen3 Coder a3b - 480b DISTILL - LM STUDIO (TOOL USE)",
+  "changed": false,
+  "operation": {
+    "fields": [
+      {
+        "key": "llm.prediction.systemPrompt",
+        "value": "TOOL USE RULES\n- If you decide to call a tool, output the tool call ONLY. Do not output any other text in the same message.\n- Do NOT print control tokens like <start_of_turn>user or <start_of_turn>model in your output.\n- After a successful tool call, WAIT for the tool result. Do not immediately call the tool again unless the previous call failed or returned nextThoughtNeeded=true and you have NEW parameters.\n- Never call the same tool twice in a row with identical parameters.\n- After summarizing a tool result once, STOP."
+      },
+      {
+        "key": "llm.prediction.promptTemplate",
+        "value": {
+          "type": "jinja",
+          "jinjaPromptTemplate": {
+            "template": "{{ bos_token }}\n{%- if messages and messages[0]['role'] == 'system' -%}\n    {%- set first_user_prefix = messages[0]['content'] ~ '\\n\\n' -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = '' -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}\n    {{ '<start_of_turn>' ~ role ~ '\\n' ~ (first_user_prefix if loop.first else '') }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<start_of_image>' }}\n            {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- elif item['type'] == 'tool_call' -%}\n                ```tool_code\n                {{ item['code'] | trim }}\n                ```\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception('Invalid content type') }}\n    {%- endif -%}\n    {{ '<end_of_turn>\\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt and (loop_messages | length == 0 or loop_messages[-1]['role'] == 'user') -%}\n    {{ '<start_of_turn>model\\n' }}\n{%- endif -%}"
+          },
+          "stopStrings": [
+            "<end_of_turn>",
+            "<start_of_turn>user",
+            "<start_of_turn>model",
+            "<start_of_turn>tool"
+          ],
+          "manualPromptTemplate": {
+            "beforeSystem": "<|im_start|>system\n",
+            "afterSystem": "<|im_end|>\n",
+            "beforeUser": "<|im_start|>user\n",
+            "afterUser": "<|im_end|>\n",
+            "beforeAssistant": "<|im_start|>assistant\n",
+            "afterAssistant": "<|im_end|>\n"
+          }
+        }
+      },
+      {
+        "key": "llm.prediction.topPSampling",
+        "value": {
+          "checked": true,
+          "value": 0.8
+        }
+      },
+      {
+        "key": "llm.prediction.topKSampling",
+        "value": 20
+      },
+      {
+        "key": "llm.prediction.temperature",
+        "value": 0.7
+      },
+      {
+        "key": "llm.prediction.repeatPenalty",
+        "value": {
+          "checked": true,
+          "value": 1.05
+        }
+      }
+    ]
+  },
+  "load": {
+    "fields": []
+  }
+}

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,33 @@
+{{ bos_token }}
+{%- if messages and messages[0]['role'] == 'system' -%}
+    {%- set first_user_prefix = messages[0]['content'] ~ '\n\n' -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = '' -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%}
+    {{ '<start_of_turn>' ~ role ~ '\n' ~ (first_user_prefix if loop.first else '') }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- elif item['type'] == 'tool_call' -%}
+                ```tool_code
+                {{ item['code'] | trim }}
+                ```
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception('Invalid content type') }}
+    {%- endif -%}
+    {{ '<end_of_turn>\n' }}
+{%- endfor -%}
+{%- if add_generation_prompt and (loop_messages | length == 0 or loop_messages[-1]['role'] == 'user') -%}
+    {{ '<start_of_turn>model\n' }}
+{%- endif -%}

config.json

@@ -0,0 +1,433 @@
+{
+    "architectures": [
+        "Qwen3MoeForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "decoder_sparse_step": 1,
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 5472,
+    "max_position_embeddings": 262144,
+    "max_window_layers": 28,
+    "mlp_only_layers": [],
+    "model_type": "qwen3_moe",
+    "moe_intermediate_size": 768,
+    "norm_topk_prob": true,
+    "num_attention_heads": 32,
+    "num_experts": 128,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 48,
+    "num_key_value_heads": 4,
+    "output_router_logits": false,
+    "qkv_bias": false,
+    "quantization": {
+        "group_size": 32,
+        "bits": 4,
+        "mode": "affine",
+        "model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "quantization_config": {
+        "group_size": 32,
+        "bits": 4,
+        "mode": "affine",
+        "model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 10000000,
+    "router_aux_loss_coef": 0.0,
+    "shared_expert_intermediate_size": 0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.52.3",
+    "use_cache": true,
+    "use_qk_norm": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20
+}

model-00001-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4fb65aa867a695472c5e167d484332cdfcd6053553f36dc476c92665108c31a9
+size 5271030693

model-00002-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b838beb2bf7d79a16f8dccec9e5a4f7f5097a39369611fe043d55d60469e2545
+size 5316125765

model-00003-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4a4383af06ad95c82f3c00400042bfe26a33551016851e2b3278562d2389d52
+size 5328211659

model-00004-of-00004.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08b8bedf6e8d4c508afb7f00950cf8bab348c1d432cb807c01b8a01f1f86c8a8
+size 3173162067

qwen3coder_tool_parser.py

@@ -0,0 +1,689 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import ast
+import json
+import uuid
+from collections.abc import Sequence
+from typing import Any, List, Optional, Union
+
+import regex as re
+
+from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
+                                              ChatCompletionToolsParam,
+                                              DeltaFunctionCall, DeltaMessage,
+                                              DeltaToolCall,
+                                              ExtractedToolCallInformation,
+                                              FunctionCall, ToolCall)
+from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
+    ToolParser, ToolParserManager)
+from vllm.logger import init_logger
+from vllm.transformers_utils.tokenizer import AnyTokenizer
+
+logger = init_logger(__name__)
+
+
+@ToolParserManager.register_module("qwen3_coder")
+class Qwen3CoderToolParser(ToolParser):
+
+    def __init__(self, tokenizer: AnyTokenizer):
+        super().__init__(tokenizer)
+
+        self.current_tool_name_sent: bool = False
+        self.prev_tool_call_arr: list[dict] = []
+        self.current_tool_id: int = -1
+        self.streamed_args_for_tool: list[str] = []
+
+        # Sentinel tokens for streaming mode
+        self.tool_call_start_token: str = "<tool_call>"
+        self.tool_call_end_token: str = "</tool_call>"
+        self.tool_call_prefix: str = "<function="
+        self.function_end_token: str = "</function>"
+        self.parameter_prefix: str = "<parameter="
+        self.parameter_end_token: str = "</parameter>"
+        self.is_tool_call_started: bool = False
+        self.failed_count: int = 0
+
+        # Enhanced streaming state - reset for each new message
+        self._reset_streaming_state()
+
+        # Regex patterns
+        self.tool_call_complete_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>", re.DOTALL)
+        self.tool_call_regex = re.compile(
+            r"<tool_call>(.*?)</tool_call>|<tool_call>(.*?)$", re.DOTALL)
+        self.tool_call_function_regex = re.compile(
+            r"<function=(.*?)</function>|<function=(.*)$", re.DOTALL)
+        self.tool_call_parameter_regex = re.compile(
+            r"<parameter=(.*?)(?:</parameter>|(?=<parameter=)|(?=</function>)|$)",
+            re.DOTALL)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ToolParser "
+                "constructor during construction.")
+
+        self.tool_call_start_token_id = self.vocab.get(
+            self.tool_call_start_token)
+        self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
+
+        if self.tool_call_start_token_id is None or self.tool_call_end_token_id is None:
+            raise RuntimeError(
+                "Qwen3 XML Tool parser could not locate tool call start/end "
+                "tokens in the tokenizer!")
+
+        logger.info(
+            f"vLLM Successfully import tool parser {self.__class__.__name__} !"
+        )
+
+    def _generate_tool_call_id(self) -> str:
+        """Generate a unique tool call ID."""
+        return f"call_{uuid.uuid4().hex[:24]}"
+
+    def _reset_streaming_state(self):
+        """Reset all streaming state."""
+        self.current_tool_index = 0
+        self.is_tool_call_started = False
+        self.header_sent = False
+        self.current_tool_id = None
+        self.current_function_name = None
+        self.current_param_name = None
+        self.current_param_value = ""
+        self.param_count = 0
+        self.in_param = False
+        self.in_function = False
+        self.accumulated_text = ""
+        self.json_started = False
+        self.json_closed = False
+        # Store accumulated parameters for type conversion
+        self.accumulated_params = {}
+        self.streaming_request = None
+
+    def _get_arguments_config(
+            self, func_name: str,
+            tools: Optional[list[ChatCompletionToolsParam]]) -> dict:
+        """Extract argument configuration for a function."""
+        if tools is None:
+            return {}
+        for config in tools:
+            if not hasattr(config, "type") or not (hasattr(
+                    config, "function") and hasattr(config.function, "name")):
+                continue
+            if config.type == "function" and config.function.name == func_name:
+                if not hasattr(config.function, "parameters"):
+                    return {}
+                params = config.function.parameters
+                if isinstance(params, dict) and "properties" in params:
+                    return params["properties"]
+                elif isinstance(params, dict):
+                    return params
+                else:
+                    return {}
+        logger.warning(f"Tool '{func_name}' is not defined in the tools list.")
+        return {}
+
+    def _convert_param_value(self, param_value: str, param_name: str,
+                             param_config: dict, func_name: str) -> Any:
+        """Convert parameter value based on its type in the schema."""
+        # Handle null value for any type
+        if param_value.lower() == "null":
+            return None
+
+        if param_name not in param_config:
+            if param_config != {}:
+                logger.warning(
+                    f"Parsed parameter '{param_name}' is not defined in the tool "
+                    f"parameters for tool '{func_name}', directly returning the string value."
+                )
+            return param_value
+
+        if isinstance(param_config[param_name],
+                      dict) and "type" in param_config[param_name]:
+            param_type = str(param_config[param_name]["type"]).strip().lower()
+        else:
+            param_type = "string"
+        if param_type in ["string", "str", "text", "varchar", "char", "enum"]:
+            return param_value
+        elif param_type.startswith("int") or param_type.startswith(
+                "uint") or param_type.startswith(
+                    "long") or param_type.startswith(
+                        "short") or param_type.startswith("unsigned"):
+            try:
+                param_value = int(param_value)
+            except:
+                logger.warning(
+                    f"Parsed value '{param_value}' of parameter '{param_name}' is not an integer in tool "
+                    f"'{func_name}', degenerating to string.")
+            return param_value
+        elif param_type.startswith("num") or param_type.startswith("float"):
+            try:
+                float_param_value = float(param_value)
+                param_value = float_param_value if float_param_value - int(
+                    float_param_value) != 0 else int(float_param_value)
+            except:
+                logger.warning(
+                    f"Parsed value '{param_value}' of parameter '{param_name}' is not a float in tool "
+                    f"'{func_name}', degenerating to string.")
+            return param_value
+        elif param_type in ["boolean", "bool", "binary"]:
+            param_value = param_value.lower()
+            if param_value not in ["true", "false"]:
+                logger.warning(
+                    f"Parsed value '{param_value}' of parameter '{param_name}' is not a boolean (`true` of `false`) in tool '{func_name}', degenerating to false."
+                )
+            return param_value == "true"
+        else:
+            if param_type in ["object", "array", "arr"
+                              ] or param_type.startswith(
+                                  "dict") or param_type.startswith("list"):
+                try:
+                    param_value = json.loads(param_value)
+                    return param_value
+                except:
+                    logger.warning(
+                        f"Parsed value '{param_value}' of parameter '{param_name}' cannot be parsed with json.loads in tool "
+                        f"'{func_name}', will try other methods to parse it.")
+            try:
+                param_value = ast.literal_eval(param_value)  # safer
+            except:
+                logger.warning(
+                    f"Parsed value '{param_value}' of parameter '{param_name}' cannot be converted via Python `ast.literal_eval()` in tool '{func_name}', degenerating to string."
+                )
+            return param_value
+
+    def _parse_xml_function_call(
+            self, function_call_str: str,
+            tools: Optional[list[ChatCompletionToolsParam]]
+    ) -> Optional[ToolCall]:
+
+        # Extract function name
+        end_index = function_call_str.index(">")
+        function_name = function_call_str[:end_index]
+        param_config = self._get_arguments_config(function_name, tools)
+        parameters = function_call_str[end_index + 1:]
+        param_dict = {}
+        for match_text in self.tool_call_parameter_regex.findall(parameters):
+            idx = match_text.index(">")
+            param_name = match_text[:idx]
+            param_value = str(match_text[idx + 1:])
+            # Remove prefix and trailing \n
+            if param_value.startswith("\n"):
+                param_value = param_value[1:]
+            if param_value.endswith("\n"):
+                param_value = param_value[:-1]
+
+            param_dict[param_name] = self._convert_param_value(
+                param_value, param_name, param_config, function_name)
+        return ToolCall(
+            type="function",
+            function=FunctionCall(name=function_name,
+                                  arguments=json.dumps(param_dict,
+                                                       ensure_ascii=False)),
+        )
+
+    def _get_function_calls(self, model_output: str) -> List[str]:
+        # Find all tool calls
+        matched_ranges = self.tool_call_regex.findall(model_output)
+        raw_tool_calls = [
+            match[0] if match[0] else match[1] for match in matched_ranges
+        ]
+
+        # Back-off strategy if no tool_call tags found
+        if len(raw_tool_calls) == 0:
+            raw_tool_calls = [model_output]
+
+        raw_function_calls = []
+        for tool_call in raw_tool_calls:
+            raw_function_calls.extend(
+                self.tool_call_function_regex.findall(tool_call))
+
+        function_calls = [
+            match[0] if match[0] else match[1] for match in raw_function_calls
+        ]
+        return function_calls
+
+    def extract_tool_calls(
+        self,
+        model_output: str,
+        request: ChatCompletionRequest,
+    ) -> ExtractedToolCallInformation:
+        # Quick check to avoid unnecessary processing
+        if self.tool_call_prefix not in model_output:
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+        try:
+            function_calls = self._get_function_calls(model_output)
+            if len(function_calls) == 0:
+                return ExtractedToolCallInformation(tools_called=False,
+                                                    tool_calls=[],
+                                                    content=model_output)
+
+            tool_calls = [
+                self._parse_xml_function_call(function_call_str, request.tools)
+                for function_call_str in function_calls
+            ]
+
+            # Populate prev_tool_call_arr for serving layer to set finish_reason
+            self.prev_tool_call_arr.clear()  # Clear previous calls
+            for tool_call in tool_calls:
+                if tool_call:
+                    self.prev_tool_call_arr.append({
+                        "name":
+                        tool_call.function.name,
+                        "arguments":
+                        tool_call.function.arguments,
+                    })
+
+            # Extract content before tool calls
+            content_index = model_output.find(self.tool_call_start_token)
+            content_index = content_index if content_index >= 0 else model_output.find(
+                self.tool_call_prefix)
+            content = model_output[:content_index]  # .rstrip()
+
+            return ExtractedToolCallInformation(
+                tools_called=(len(tool_calls) > 0),
+                tool_calls=tool_calls,
+                content=content if content else None,
+            )
+
+        except Exception:
+            logger.exception("Error in extracting tool call from response.")
+            return ExtractedToolCallInformation(tools_called=False,
+                                                tool_calls=[],
+                                                content=model_output)
+
+    def extract_tool_calls_streaming(
+        self,
+        previous_text: str,
+        current_text: str,
+        delta_text: str,
+        previous_token_ids: Sequence[int],
+        current_token_ids: Sequence[int],
+        delta_token_ids: Sequence[int],
+        request: ChatCompletionRequest,
+    ) -> Union[DeltaMessage, None]:
+        # Store request for type conversion
+        if not previous_text:
+            self._reset_streaming_state()
+            self.streaming_request = request
+
+        # If no delta text, return None unless it's an EOS token after tool calls
+        if not delta_text:
+            # Check if this is an EOS token after all tool calls are complete
+            # We check for tool calls in the text even if is_tool_call_started is False
+            # because it might have been reset after processing all tools
+            if delta_token_ids and self.tool_call_end_token_id not in delta_token_ids:
+                # Count complete tool calls
+                complete_calls = len(
+                    self.tool_call_complete_regex.findall(current_text))
+
+                # If we have completed tool calls and populated prev_tool_call_arr
+                if complete_calls > 0 and len(self.prev_tool_call_arr) > 0:
+                    # Check if all tool calls are closed
+                    open_calls = current_text.count(
+                        self.tool_call_start_token) - current_text.count(
+                            self.tool_call_end_token)
+                    if open_calls == 0:
+                        # Return empty delta message to allow finish_reason processing
+                        return DeltaMessage(content="")
+                elif not self.is_tool_call_started and current_text:
+                    # This is a regular content response that's now complete
+                    return DeltaMessage(content="")
+            return None
+
+        # Update accumulated text
+        self.accumulated_text = current_text
+
+        # Check if we need to advance to next tool
+        if self.json_closed and not self.in_function:
+            # Check if this tool call has ended
+            tool_ends = current_text.count(self.tool_call_end_token)
+            if tool_ends > self.current_tool_index:
+                # This tool has ended, advance to next
+                self.current_tool_index += 1
+                self.header_sent = False
+                self.param_count = 0
+                self.json_started = False
+                self.json_closed = False
+                self.accumulated_params = {}
+
+                # Check if there are more tool calls
+                tool_starts = current_text.count(self.tool_call_start_token)
+                if self.current_tool_index >= tool_starts:
+                    # No more tool calls
+                    self.is_tool_call_started = False
+                # Continue processing next tool
+                return None
+
+        # Handle normal content before tool calls
+        if not self.is_tool_call_started:
+            # Check if tool call is starting
+            if self.tool_call_start_token_id in delta_token_ids or self.tool_call_start_token in delta_text:
+                self.is_tool_call_started = True
+                # Return any content before the tool call
+                if self.tool_call_start_token in delta_text:
+                    content_before = delta_text[:delta_text.index(
+                        self.tool_call_start_token)]
+                    if content_before:
+                        return DeltaMessage(content=content_before)
+                return None
+            else:
+                # Check if we're between tool calls - skip whitespace
+                if current_text.rstrip().endswith(self.tool_call_end_token):
+                    # We just ended a tool call, skip whitespace
+                    if delta_text.strip() == "":
+                        return None
+                # Normal content, no tool call
+                return DeltaMessage(content=delta_text)
+
+        # Check if we're between tool calls (waiting for next one)
+        # Count tool calls we've seen vs processed
+        tool_starts_count = current_text.count(self.tool_call_start_token)
+        if self.current_tool_index >= tool_starts_count:
+            # We're past all tool calls, shouldn't be here
+            return None
+
+        # We're in a tool call, find the current tool call portion
+        # Need to find the correct tool call based on current_tool_index
+        tool_starts = []
+        idx = 0
+        while True:
+            idx = current_text.find(self.tool_call_start_token, idx)
+            if idx == -1:
+                break
+            tool_starts.append(idx)
+            idx += len(self.tool_call_start_token)
+
+        if self.current_tool_index >= len(tool_starts):
+            # No more tool calls to process yet
+            return None
+
+        tool_start_idx = tool_starts[self.current_tool_index]
+        # Find where this tool call ends (or current position if not ended yet)
+        tool_end_idx = current_text.find(self.tool_call_end_token,
+                                         tool_start_idx)
+        if tool_end_idx == -1:
+            tool_text = current_text[tool_start_idx:]
+        else:
+            tool_text = current_text[tool_start_idx:tool_end_idx +
+                                     len(self.tool_call_end_token)]
+
+        # Looking for function header
+        if not self.header_sent:
+            if self.tool_call_prefix in tool_text:
+                func_start = tool_text.find(self.tool_call_prefix) + len(
+                    self.tool_call_prefix)
+                func_end = tool_text.find(">", func_start)
+
+                if func_end != -1:
+                    # Found complete function name
+                    self.current_function_name = tool_text[func_start:func_end]
+                    self.current_tool_id = self._generate_tool_call_id()
+                    self.header_sent = True
+                    self.in_function = True
+
+                    # IMPORTANT: Add to prev_tool_call_arr immediately when we detect a tool call
+                    # This ensures finish_reason="tool_calls" even if parsing isn't complete
+                    already_added = any(
+                        tool.get("name") == self.current_function_name
+                        for tool in self.prev_tool_call_arr)
+                    if not already_added:
+                        self.prev_tool_call_arr.append({
+                            "name": self.current_function_name,
+                            "arguments":
+                            "{}",  # Placeholder, will be updated later
+                        })
+
+                    # Send header with function info
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_index,
+                            id=self.current_tool_id,
+                            function=DeltaFunctionCall(
+                                name=self.current_function_name, arguments=""),
+                            type="function",
+                        )
+                    ])
+            return None
+
+        # We've sent header, now handle function body
+        if self.in_function:
+            # Send opening brace if not sent yet
+            if not self.json_started and self.parameter_prefix not in delta_text:
+                self.json_started = True
+                return DeltaMessage(tool_calls=[
+                    DeltaToolCall(
+                        index=self.current_tool_index,
+                        function=DeltaFunctionCall(arguments="{"),
+                    )
+                ])
+
+            # Make sure json_started is set if we're processing parameters
+            if not self.json_started:
+                self.json_started = True
+
+            # Check for function end in accumulated text
+            if not self.json_closed and self.function_end_token in tool_text:
+                # Close JSON
+                self.json_closed = True
+
+                # Extract the complete tool call to update prev_tool_call_arr with final arguments
+                # Find the function content
+                func_start = tool_text.find(self.tool_call_prefix) + len(
+                    self.tool_call_prefix)
+                func_content_end = tool_text.find(self.function_end_token,
+                                                  func_start)
+                if func_content_end != -1:
+                    func_content = tool_text[func_start:func_content_end]
+                    # Parse to get the complete arguments
+                    try:
+                        parsed_tool = self._parse_xml_function_call(
+                            func_content, self.streaming_request.tools
+                            if self.streaming_request else None)
+                        if parsed_tool:
+                            # Update existing entry in prev_tool_call_arr with complete arguments
+                            for i, tool in enumerate(self.prev_tool_call_arr):
+                                if tool.get(
+                                        "name") == parsed_tool.function.name:
+                                    self.prev_tool_call_arr[i][
+                                        "arguments"] = parsed_tool.function.arguments
+                                    break
+                    except Exception:
+                        pass  # Ignore parsing errors during streaming
+
+                result = DeltaMessage(tool_calls=[
+                    DeltaToolCall(
+                        index=self.current_tool_index,
+                        function=DeltaFunctionCall(arguments="}"),
+                    )
+                ])
+
+                # Reset state for next tool
+                self.in_function = False
+                self.json_closed = True
+                self.accumulated_params = {}
+
+                return result
+
+            # Look for parameters
+            # Find all parameter starts
+            param_starts = []
+            idx = 0
+            while True:
+                idx = tool_text.find(self.parameter_prefix, idx)
+                if idx == -1:
+                    break
+                param_starts.append(idx)
+                idx += len(self.parameter_prefix)
+
+            # Check if we should start a new parameter
+            if not self.in_param and self.param_count < len(param_starts):
+
+                if len(param_starts) > self.param_count:
+                    # Process the next parameter
+                    param_idx = param_starts[self.param_count]
+                    param_start = param_idx + len(self.parameter_prefix)
+                    remaining = tool_text[param_start:]
+
+                    if ">" in remaining:
+                        # We have the complete parameter name
+                        name_end = remaining.find(">")
+                        self.current_param_name = remaining[:name_end]
+
+                        # Find the parameter value
+                        value_start = param_start + name_end + 1
+                        value_text = tool_text[value_start:]
+                        if value_text.startswith("\n"):
+                            value_text = value_text[1:]
+
+                        # Find where this parameter ends
+                        param_end_idx = value_text.find(
+                            self.parameter_end_token)
+                        if param_end_idx == -1:
+                            # No closing tag, look for next parameter or function end
+                            next_param_idx = value_text.find(
+                                self.parameter_prefix)
+                            func_end_idx = value_text.find(
+                                self.function_end_token)
+
+                            if next_param_idx != -1 and (func_end_idx == -1
+                                                         or next_param_idx
+                                                         < func_end_idx):
+                                param_end_idx = next_param_idx
+                            elif func_end_idx != -1:
+                                param_end_idx = func_end_idx
+                            else:
+                                # Neither found, check if tool call is complete
+                                if self.tool_call_end_token in tool_text:
+                                    # Tool call is complete, so parameter must be complete too
+                                    # Use all remaining text before function end as value
+                                    param_end_idx = len(value_text)
+                                else:
+                                    # Still streaming, wait for more content
+                                    return None
+
+                        if param_end_idx != -1:
+                            # Complete parameter found
+                            param_value = value_text[:param_end_idx]
+                            if param_value.endswith("\n"):
+                                param_value = param_value[:-1]
+
+                            # Store raw value for later processing
+                            self.accumulated_params[
+                                self.current_param_name] = param_value
+
+                            # Get parameter configuration for type conversion
+                            param_config = self._get_arguments_config(
+                                self.current_function_name,
+                                self.streaming_request.tools
+                                if self.streaming_request else None)
+
+                            # Convert the parameter value to the appropriate type
+                            converted_value = self._convert_param_value(
+                                param_value, self.current_param_name,
+                                param_config, self.current_function_name)
+
+                            # Build JSON fragment based on the converted type
+                            # Use json.dumps to properly serialize the value
+                            serialized_value = json.dumps(converted_value,
+                                                          ensure_ascii=False)
+
+                            if self.param_count == 0:
+                                json_fragment = f'"{self.current_param_name}": {serialized_value}'
+                            else:
+                                json_fragment = f', "{self.current_param_name}": {serialized_value}'
+
+                            self.param_count += 1
+
+                            return DeltaMessage(tool_calls=[
+                                DeltaToolCall(
+                                    index=self.current_tool_index,
+                                    function=DeltaFunctionCall(
+                                        arguments=json_fragment),
+                                )
+                            ])
+
+            # Continue parameter value - Not used in the current implementation
+            # since we process complete parameters above
+            if self.in_param:
+                if self.parameter_end_token in delta_text:
+                    # End of parameter
+                    end_idx = delta_text.find(self.parameter_end_token)
+                    value_chunk = delta_text[:end_idx]
+
+                    # Skip past > if at start
+                    if not self.current_param_value and ">" in value_chunk:
+                        gt_idx = value_chunk.find(">")
+                        value_chunk = value_chunk[gt_idx + 1:]
+
+                    if not self.current_param_value and value_chunk.startswith(
+                            "\n"):
+                        value_chunk = value_chunk[1:]
+
+                    # Store complete value
+                    full_value = self.current_param_value + value_chunk
+                    self.accumulated_params[
+                        self.current_param_name] = full_value
+
+                    # Get parameter configuration for type conversion
+                    param_config = self._get_arguments_config(
+                        self.current_function_name,
+                        self.streaming_request.tools
+                        if self.streaming_request else None)
+
+                    # Convert the parameter value to the appropriate type
+                    converted_value = self._convert_param_value(
+                        full_value, self.current_param_name, param_config,
+                        self.current_function_name)
+
+                    # Serialize the converted value
+                    serialized_value = json.dumps(converted_value,
+                                                  ensure_ascii=False)
+
+                    # Since we've been streaming the quoted version, we need to close it properly
+                    # This is complex - for now just complete the value
+                    self.in_param = False
+                    self.current_param_value = ""
+
+                    # Just close the current parameter string
+                    return DeltaMessage(tool_calls=[
+                        DeltaToolCall(
+                            index=self.current_tool_index,
+                            function=DeltaFunctionCall(
+                                arguments='"'),  # Close the string quote
+                        )
+                    ])
+                else:
+                    # Continue accumulating value
+                    value_chunk = delta_text
+
+                    # Handle first chunk after param name
+                    if not self.current_param_value and ">" in value_chunk:
+                        gt_idx = value_chunk.find(">")
+                        value_chunk = value_chunk[gt_idx + 1:]
+
+                    if not self.current_param_value and value_chunk.startswith(
+                            "\n"):
+                        value_chunk = value_chunk[1:]
+
+                    if value_chunk:
+                        # Stream the escaped delta
+                        prev_escaped = json.dumps(
+                            self.current_param_value, ensure_ascii=False
+                        )[1:-1] if self.current_param_value else ""
+                        self.current_param_value += value_chunk
+                        full_escaped = json.dumps(self.current_param_value,
+                                                  ensure_ascii=False)[1:-1]
+                        delta_escaped = full_escaped[len(prev_escaped):]
+
+                        if delta_escaped:
+                            return DeltaMessage(tool_calls=[
+                                DeltaToolCall(
+                                    index=self.current_tool_index,
+                                    function=DeltaFunctionCall(
+                                        arguments=delta_escaped),
+                                )
+                            ])
+
+        return None

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "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
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1048576,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}