Add files using upload-large-folder tool

.gitattributes

@@ -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

README.md

@@ -0,0 +1,37 @@
+---
+library_name: mlx
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- mlx
+base_model: Qwen/Qwen3-Coder-Next
+---
+
+# mlx-community/Qwen3-Coder-Next-4bit
+
+This model [mlx-community/Qwen3-Coder-Next-4bit](https://huggingface.co/mlx-community/Qwen3-Coder-Next-4bit) was
+converted to MLX format from [Qwen/Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next)
+using mlx-lm version **0.30.6**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("mlx-community/Qwen3-Coder-Next-4bit")
+
+prompt = "hello"
+
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, return_dict=False,
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```

chat_template.jinja

@@ -0,0 +1,117 @@
+{% macro render_extra_keys(json_dict, handled_keys) %}
+    {%- if json_dict is mapping %}
+        {%- for json_key in json_dict if json_key not in handled_keys %}
+            {%- if json_dict[json_key] is string %}
+                {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{%- endmacro %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks." }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {%- set handled_keys = ['type', 'properties'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
+            {{- '\n' + message.content | trim + '\n' }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+            {%- if tool_call.arguments is defined %}
+                {%- for args_name, args_value in tool_call.arguments|items %}
+                    {{- '<parameter=' + args_name + '>\n' }}
+                    {%- set args_value = args_value if args_value is string else args_value | tojson | safe %}
+                    {{- args_value }}
+                    {{- '\n</parameter>\n' }}
+                {%- endfor %}
+            {%- endif %}
+            {{- '</function>\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "user" or message.role == "system" or message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,825 @@
+{
+    "architectures": [
+        "Qwen3NextForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0,
+    "bos_token_id": 151643,
+    "decoder_sparse_step": 1,
+    "eos_token_id": [
+        151645,
+        151643
+    ],
+    "full_attention_interval": 4,
+    "head_dim": 256,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "linear_conv_kernel_dim": 4,
+    "linear_key_head_dim": 128,
+    "linear_num_key_heads": 16,
+    "linear_num_value_heads": 32,
+    "linear_value_head_dim": 128,
+    "max_position_embeddings": 262144,
+    "mlp_only_layers": [],
+    "model_type": "qwen3_next",
+    "moe_intermediate_size": 512,
+    "norm_topk_prob": true,
+    "num_attention_heads": 16,
+    "num_experts": 512,
+    "num_experts_per_tok": 10,
+    "num_hidden_layers": 48,
+    "num_key_value_heads": 2,
+    "output_router_logits": false,
+    "partial_rotary_factor": 0.25,
+    "quantization": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine",
+        "model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.0.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "quantization_config": {
+        "group_size": 64,
+        "bits": 4,
+        "mode": "affine",
+        "model.layers.0.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.0.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.1.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.2.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.3.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.4.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.5.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.6.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.7.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.8.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.9.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.10.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.11.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.12.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.13.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.14.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.15.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.16.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.17.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.18.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.19.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.20.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.21.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.22.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.23.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.24.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.25.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.26.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.27.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.28.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.29.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.30.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.31.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.32.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.33.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.34.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.35.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.36.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.37.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.38.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.39.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.40.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.41.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.42.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.43.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.44.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.45.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.46.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.gate": {
+            "group_size": 64,
+            "bits": 8
+        },
+        "model.layers.47.mlp.shared_expert_gate": {
+            "group_size": 64,
+            "bits": 8
+        }
+    },
+    "rms_norm_eps": 1e-06,
+    "rope_scaling": null,
+    "rope_theta": 5000000,
+    "router_aux_loss_coef": 0.001,
+    "shared_expert_intermediate_size": 512,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.57.0.dev0",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 1.0,
+  "top_k": 40,
+  "top_p": 0.95,
+  "transformers_version": "4.57.3"
+}

model-00001-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb30a2722004cb427d9dc3b3583a9dc4f94badf68420393324e6ebee4432c96f
+size 5132893190

model-00002-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6b2a95a4bb7463f74951a5565e2504e7f28fe78877bb966378003b0def69038
+size 5257948500

model-00003-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0b8e009bd09fb5994d4d188a61e28a90aead48a50d7e0983d3bd7fc3644553d3
+size 5239714716

model-00004-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b34fd01a609db8bd4e445dcc0cc1ef5991550523b4df7e89f59c7d50343e321
+size 5261626153

model-00005-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f19b843ee11c5b4c8d56b2a5940033e81ec29013de75d944447754238344621b
+size 5257948651

model-00006-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37f9b2ff202e8d58239f1df6acc568cb08e10a324b57c19f21b38ce93608b8c0
+size 5239714662

model-00007-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:78e4b432212f783db393bc643b9875918fc050c3d3c41a071bf9260449db356a
+size 5257948739

model-00008-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3841f088f9ceed8f4024e0c6496677af6b6a32047f762337b6d42232261cb3dc
+size 5261626157

model-00009-of-00009.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:933efe2e2f28f97b9b851839c43fca0d6742b26c9ff9df1fd0e427712246553c
+size 2934865732

qwen3_coder_detector_sgl.py

@@ -0,0 +1,474 @@
+import ast
+import json
+import logging
+import re
+from typing import Any, List, Optional
+
+from sglang.srt.entrypoints.openai.protocol import Tool
+from sglang.srt.function_call.base_format_detector import BaseFormatDetector
+from sglang.srt.function_call.core_types import (
+    StreamingParseResult,
+    ToolCallItem,
+    _GetInfoFunc,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class Qwen3CoderDetector(BaseFormatDetector):
+    def __init__(self):
+        super().__init__()
+
+        # Sentinel tokens
+        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>"
+
+        # Regex for non-streaming fallback
+        self.tool_call_regex = re.compile(r"<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,
+        )
+
+        # Streaming State
+        # Base class already initializes _buffer, we just use it directly
+        # No need to check with hasattr - we control the lifecycle through inheritance
+
+        # Index pointing to the next character to be processed in buffer
+        self.parsed_pos: int = 0
+        # Parameter count inside the current tool being processed, used to determine whether to add comma
+        self.current_tool_param_count: int = 0
+        # Flag indicating whether current tool has already sent '{'
+        self.json_started: bool = False
+
+        # [FIX] New state flag: mark whether inside tool_call structure block
+        self.is_inside_tool_call: bool = False
+
+        # Initialize attributes that were missing in the original PR
+        self.current_func_name: Optional[str] = None
+
+    def has_tool_call(self, text: str) -> bool:
+        return self.tool_call_start_token in text
+
+    def _get_arguments_config(
+        self, func_name: str, tools: Optional[list[Tool]]
+    ) -> dict:
+        """Extract argument configuration for a function."""
+        if tools is None:
+            return {}
+        for config in tools:
+            try:
+                config_type = config.type
+                config_function = config.function
+                config_function_name = config_function.name
+            except AttributeError:
+                continue
+
+            if config_type == "function" and config_function_name == func_name:
+                try:
+                    params = config_function.parameters
+                except AttributeError:
+                    return {}
+
+                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 Exception:
+                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:
+                maybe_convert = (
+                    False if "." in param_value or "e" in param_value.lower() else True
+                )
+                param_value: float = float(param_value)
+                if maybe_convert and param_value.is_integer():
+                    param_value = int(param_value)
+            except Exception:
+                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 Exception:
+                    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 Exception:
+                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 detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
+        """One-shot parsing for non-streaming scenarios."""
+        if self.tool_call_start_token not in text:
+            return StreamingParseResult(normal_text=text)
+
+        calls = []
+        try:
+            # Simple cleanup of the text to find tool calls
+            # Note: This is a simplified regex approach consistent with vLLM
+            raw_tool_calls = self.tool_call_regex.findall(text)
+            if not raw_tool_calls:
+                # Fallback: maybe the whole text is inside the tag or tags are stripped
+                if self.tool_call_prefix in text:
+                    raw_tool_calls = [text]
+
+            tool_idx = 0
+            for tool_content in raw_tool_calls:
+                # Find function calls
+                funcs = self.tool_call_function_regex.findall(tool_content)
+                for func_match in funcs:
+                    func_body = func_match[0] or func_match[1]
+                    if ">" not in func_body:
+                        continue
+
+                    name_end = func_body.index(">")
+                    func_name = func_body[:name_end]
+                    params_str = func_body[name_end + 1 :]
+
+                    param_config = self._get_arguments_config(func_name, tools)
+                    parsed_params = {}
+
+                    for p_match in self.tool_call_parameter_regex.findall(params_str):
+                        if ">" not in p_match:
+                            continue
+                        p_idx = p_match.index(">")
+                        p_name = p_match[:p_idx]
+                        p_val = p_match[p_idx + 1 :]
+                        # Remove prefixing and trailing \n
+                        if p_val.startswith("\n"):
+                            p_val = p_val[1:]
+                        if p_val.endswith("\n"):
+                            p_val = p_val[:-1]
+
+                        parsed_params[p_name] = self._convert_param_value(
+                            p_val, p_name, param_config, func_name
+                        )
+
+                    calls.append(
+                        ToolCallItem(
+                            tool_index=tool_idx,
+                            name=func_name,
+                            parameters=json.dumps(parsed_params, ensure_ascii=False),
+                        )
+                    )
+                    tool_idx += 1
+
+            # Determine normal text (text before the first tool call)
+            start_idx = text.find(self.tool_call_start_token)
+            if start_idx == -1:
+                start_idx = text.find(self.tool_call_prefix)
+            normal_text = text[:start_idx] if start_idx > 0 else ""
+
+            return StreamingParseResult(normal_text=normal_text, calls=calls)
+
+        except Exception as e:
+            logger.error(f"Error in detect_and_parse: {e}")
+            return StreamingParseResult(normal_text=text)
+
+    def parse_streaming_increment(
+        self, new_text: str, tools: List[Tool]
+    ) -> StreamingParseResult:
+        """
+        Robust cursor-based streaming parser.
+        """
+        self._buffer += new_text
+
+        # Guard against empty buffer
+        if not self._buffer:
+            return StreamingParseResult()
+
+        calls = []
+        normal_text_chunks = []
+
+        while True:
+            # Working text slice
+            current_slice = self._buffer[self.parsed_pos :]
+
+            # Optimization: If almost empty, wait for more
+            if not current_slice:
+                break
+
+            # -------------------------------------------------------
+            # 1. Priority detection: check if it's the start of Tool Call
+            # -------------------------------------------------------
+            if current_slice.startswith(self.tool_call_start_token):
+                self.parsed_pos += len(self.tool_call_start_token)
+                self.is_inside_tool_call = True
+                continue
+
+            # -------------------------------------------------------
+            # 2. Function Name: <function=name>
+            # -------------------------------------------------------
+            if current_slice.startswith(self.tool_call_prefix):
+                end_angle = current_slice.find(">")
+                if end_angle != -1:
+                    func_name = current_slice[len(self.tool_call_prefix) : end_angle]
+
+                    self.current_tool_id += 1
+                    self.current_tool_name_sent = True
+                    self.current_tool_param_count = 0
+                    self.json_started = False
+                    self.current_func_name = func_name
+
+                    calls.append(
+                        ToolCallItem(
+                            tool_index=self.current_tool_id,
+                            name=func_name,
+                            parameters="",
+                        )
+                    )
+
+                    self.parsed_pos += end_angle + 1
+                    continue
+                else:
+                    # Incomplete tag
+                    break
+
+            # -------------------------------------------------------
+            # 3. Parameter: <parameter=name>value...
+            # -------------------------------------------------------
+            if current_slice.startswith(self.parameter_prefix):
+                name_end = current_slice.find(">")
+                if name_end != -1:
+                    value_start_idx = name_end + 1
+                    rest_of_slice = current_slice[value_start_idx:]
+
+                    # A parameter can end in multiple ways:
+                    # 1. [Normal] Encounter </parameter>
+                    # 2. [Abnormal] Encounter next <parameter=
+                    # 3. [Abnormal] Encounter </function>
+                    # So we need to find the smallest one as the parameter end position.
+                    cand_end_param = rest_of_slice.find(self.parameter_end_token)
+                    cand_next_param = rest_of_slice.find(self.parameter_prefix)
+                    cand_end_func = rest_of_slice.find(self.function_end_token)
+
+                    candidates = []
+                    if cand_end_param != -1:
+                        candidates.append(
+                            (cand_end_param, len(self.parameter_end_token))
+                        )
+                    if cand_next_param != -1:
+                        candidates.append((cand_next_param, 0))
+                    if cand_end_func != -1:
+                        candidates.append((cand_end_func, 0))
+
+                    if candidates:
+                        best_cand = min(candidates, key=lambda x: x[0])
+                        end_pos = best_cand[0]
+                        end_token_len = best_cand[1]
+
+                        param_name = current_slice[
+                            len(self.parameter_prefix) : name_end
+                        ]
+                        raw_value = rest_of_slice[:end_pos]
+
+                        # Cleanup value
+                        if raw_value.startswith("\n"):
+                            raw_value = raw_value[1:]
+                        if raw_value.endswith("\n"):
+                            raw_value = raw_value[:-1]
+
+                        # JSON Construction
+                        if not self.json_started:
+                            calls.append(
+                                ToolCallItem(
+                                    tool_index=self.current_tool_id, parameters="{"
+                                )
+                            )
+                            self.json_started = True
+
+                        param_config = self._get_arguments_config(
+                            self.current_func_name, tools
+                        )
+                        converted_val = self._convert_param_value(
+                            raw_value, param_name, param_config, self.current_func_name
+                        )
+
+                        # Construct JSON fragment: "key": value
+                        # Note: We must be careful with json.dumps to ensure valid JSON streaming
+                        json_key_val = f"{json.dumps(param_name)}: {json.dumps(converted_val, ensure_ascii=False)}"
+
+                        if self.current_tool_param_count > 0:
+                            fragment = f", {json_key_val}"
+                        else:
+                            fragment = json_key_val
+
+                        calls.append(
+                            ToolCallItem(
+                                tool_index=self.current_tool_id, parameters=fragment
+                            )
+                        )
+                        self.current_tool_param_count += 1
+
+                        # Advance cursor
+                        total_len = (name_end + 1) + end_pos + end_token_len
+                        self.parsed_pos += total_len
+                        continue
+
+                # Incomplete parameter tag or value
+                break
+
+            # -------------------------------------------------------
+            # 4. Function End: </function>
+            # -------------------------------------------------------
+            if current_slice.startswith(self.function_end_token):
+                if not self.json_started:
+                    calls.append(
+                        ToolCallItem(tool_index=self.current_tool_id, parameters="{")
+                    )
+                    self.json_started = True
+
+                calls.append(
+                    ToolCallItem(tool_index=self.current_tool_id, parameters="}")
+                )
+                self.parsed_pos += len(self.function_end_token)
+                self.current_func_name = None
+                continue
+
+            # -------------------------------------------------------
+            # 5. Tool Call End: </tool_call>
+            # -------------------------------------------------------
+            if current_slice.startswith(self.tool_call_end_token):
+                self.parsed_pos += len(self.tool_call_end_token)
+                self.is_inside_tool_call = False  # [FIX] Exit tool call region
+                continue
+
+            # -------------------------------------------------------
+            # 6. Handling content / whitespace / normal text
+            # -------------------------------------------------------
+            # If current position is not the start of a tag (i.e., doesn't start with <), it might be plain text,
+            # or a newline between two tags.
+            # But we need to be careful not to output truncated tags like "<fun" as text.
+
+            next_open_angle = current_slice.find("<")
+
+            if next_open_angle == -1:
+                # This entire segment is plain text
+                if not self.is_inside_tool_call:
+                    normal_text_chunks.append(current_slice)
+                # [FIX] If inside tool call, discard this text (usually \n), don't append
+                self.parsed_pos += len(current_slice)
+                continue
+
+            elif next_open_angle == 0:
+                # Looks like a Tag, but doesn't match any known Tag above
+
+                possible_tags = [
+                    self.tool_call_start_token,
+                    self.tool_call_end_token,
+                    self.tool_call_prefix,
+                    self.function_end_token,
+                    self.parameter_prefix,
+                    self.parameter_end_token,
+                ]
+
+                is_potential_tag = False
+                for tag in possible_tags:
+                    if tag.startswith(current_slice):
+                        is_potential_tag = True
+                        break
+
+                if is_potential_tag:
+                    break  # Wait for more
+                else:
+                    # Just a plain '<' symbol
+                    if not self.is_inside_tool_call:
+                        normal_text_chunks.append("<")
+                    self.parsed_pos += 1
+                    continue
+
+            else:
+                # '<' is in the middle
+                text_segment = current_slice[:next_open_angle]
+                if not self.is_inside_tool_call:
+                    normal_text_chunks.append(text_segment)
+                # [FIX] If inside tool call, discard whitespace/text before Tag
+                self.parsed_pos += next_open_angle
+                continue
+
+        # Memory Cleanup: Slice the buffer
+        # Keep unparsed part, discard parsed part
+        if self.parsed_pos > 0:
+            self._buffer = self._buffer[self.parsed_pos :]
+            self.parsed_pos = 0
+
+        normal_text = "".join(normal_text_chunks) if normal_text_chunks else ""
+        return StreamingParseResult(calls=calls, normal_text=normal_text)
+
+    def supports_structural_tag(self) -> bool:
+        return False
+
+    def structure_info(self) -> _GetInfoFunc:
+        raise NotImplementedError

qwen3coder_tool_parser_vllm.py

@@ -0,0 +1,690 @@
+# 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:
+                maybe_convert = False if "." in param_value or "e" in param_value.lower() else True
+                param_value: float = float(param_value)
+                if maybe_convert and param_value.is_integer():
+                    param_value = int(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

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
+size 11422650

tokenizer_config.json

@@ -0,0 +1,30 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_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|>"
+  ],
+  "is_local": true,
+  "model_max_length": 1048576,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "tool_parser_type": "qwen3_coder",
+  "unk_token": null
+}