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Model: doodod/Turn-Detector-Qwen3-0.6B
Source: Original Platform

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+---
+license: mit
+language:
+- zh
+- en
+base_model:
+- Qwen/Qwen3-0.6B
+---
+
+# Introduction
+
+> Voice Chat Pipeline -> ASR + TurnDetector + VAD + LLM + TTS
+
+In the Voice Chat Pipeline, if we only rely on **VAD (Voice Activity Detection)** to determine whether the user's current turn input has ended, we cannot accurately handle situations where users pause while thinking. When there are pauses during the current turn input that hasn't been completed yet, VAD will detect the pause and prematurely judge that the sentence has ended, but semantically the sentence is not yet complete.
+
+This introduces the **Turn-Detector Model**. The turn detection model is mainly applied in voice + text modal dialogue scenarios. At the semantic level, the turn detection model can analyze the text information transcribed by the ASR model at the semantic level, more accurately determining whether the current user input has ended. The Turn-Detector Model chooses small-parameter (0.5B/0.6B) large models based on Transformer architecture that have undergone instruction fine-tuning, with the main task being to predict the probability of the next_token being `<|im_end|>`.
+
+
+- **Task**: Semantic-level turn recognition, predicting the probability of next_token being `<|im_end|>`
+- **Model**: Small-parameter models after instruction fine-tuning (Qwen2.5-0.5B-Instruct, Qwen3-0.6B)
+- **Goal**: Reduce inaccurate VAD interruptions in voice dialogue pipelines (e.g., pauses caused while thinking of the next word)
+
+
+```
+# 1. get the user input
+How tall is the Eiffel Tower
+
+# 2. apply_chat_template
+<|im_start|>user<|im_sep|>How tall is the Eiffel Tower<|im_end|>
+
+# 3. cut <|im_end|>
+<|im_start|>user<|im_sep|>How tall is the Eiffel Tower
+
+# 4. predict next token
+```
+
+**Language**: Chinese, English
+
+**GitHub Page**: https://github.com/zxsddcs/Turn-Detector
+
+
+# Dataset
+
+The turn detection model is mainly applied in Chinese and English voice + text modal dialogue scenarios, with input data types mostly being common text instruction data and colloquial chat dialogue data. Therefore, the dataset uses public datasets such as Alpaca, MagicData (ASR dialogue dataset), ShareChatX, etc.
+- Alpaca
+- Magicdata
+- ShareChatX
+
+
+Characteristics of ASR transcribed text
+- Sometimes sentence endings don't contain punctuation marks
+- There may be filler words or ... during the process
+
+
+Dataset optimization based on ASR transcribed text characteristics
+- Sentence filtering: Call large models to analyze current input content, retaining semantically complete and colloquial data from the dataset
+- Filler word insertion: Randomly insert 1 filler word in sentences to simulate the actual effect of spoken dialogue
+
+Call large models to generate Chinese and English filler word tables
+```python
+en_words = ['uh', 'um', 'ah', 'er', 'hmm', ...]
+zh_words = ['嗯', '啊', '哦', '呃', '那个', '对吧', ...]
+```
+
+Dataset optimization effect example
+```json
+[
+    {
+        "instruction": "How tall is the Eiffel Tower",
+        "input": "",
+        "output": ""
+    },
+    {
+        "instruction": "How tall is the um... Eiffel Tower",
+        "input": "",
+        "output": ""
+    },
+    {
+        "instruction": "Um how tall is the Eiffel Tower",
+        "input": "",
+        "output": ""
+    },
+    ...
+]
+```
+
+
+# Quantization
+
+```python
+from optimum.onnxruntime import ORTModelForCausalLM
+from optimum.onnxruntime.configuration import AutoQuantizationConfig
+from optimum.onnxruntime import ORTQuantizer
+from transformers import AutoTokenizer
+
+model_checkpoint = ""
+save_directory = ""
+
+ort_model = ORTModelForCausalLM.from_pretrained(
+    model_checkpoint, 
+    export=True,
+    use_cache=False,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+ort_model.save_pretrained(save_directory)
+tokenizer.save_pretrained(save_directory)
+
+qconfig = AutoQuantizationConfig.avx512_vnni(is_static=False, per_channel=False)
+quantizer = ORTQuantizer.from_pretrained(ort_model)
+quantizer.quantize(save_dir=save_directory, quantization_config=qconfig)
+```
+
+
+# References
+
+## Text
+- Think Beyond VAD : End-of-Turn Detection for Voice Bot: A Python Implementation (https://medium.com/@manoranjan.rajguru/end-of-turn-detection-with-transformers-a-python-implementation-23bd74f621f3)
+- How to build smarter turn detection for Voice AI (https://blog.speechmatics.com/semantic-turn-detection#wrapping-up-where-were-headed-next)
+- LiveKit (https://docs.livekit.io/agents/build/turns/turn-detector/#overview)
+- 基于大语言模型实现文本端点检测 (https://blog.csdn.net/qq_33200967/article/details/145210382)
+
+
+## Audio
+- Speculative End-Turn Detector for Efficient Speech Chatbot Assistant (https://arxiv.org/html/2503.23439v1)
+- Smart Turn v2 (https://www.daily.co/blog/smart-turn-v2-faster-inference-and-13-new-languages-for-voice-ai/)

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|continue|>": 151669,
+  "<|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

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

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 40960,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.52.4"
+}

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ort_config.json

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+{
+  "one_external_file": true,
+  "opset": null,
+  "optimization": {},
+  "quantization": {
+    "activations_dtype": "QUInt8",
+    "activations_symmetric": false,
+    "format": "QOperator",
+    "is_static": false,
+    "mode": "IntegerOps",
+    "nodes_to_exclude": [],
+    "nodes_to_quantize": [],
+    "operators_to_quantize": [
+      "Conv",
+      "MatMul",
+      "Attention",
+      "LSTM",
+      "Gather",
+      "Transpose",
+      "EmbedLayerNormalization"
+    ],
+    "per_channel": false,
+    "qdq_add_pair_to_weight": false,
+    "qdq_dedicated_pair": false,
+    "qdq_op_type_per_channel_support_to_axis": {
+      "MatMul": 1
+    },
+    "reduce_range": false,
+    "weights_dtype": "QInt8",
+    "weights_symmetric": true
+  },
+  "use_external_data_format": false
+}

special_tokens_map.json

@@ -0,0 +1,32 @@
+{
+  "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|>",
+    "<|continue|>"
+  ],
+  "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

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+size 11422843

tokenizer_config.json

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+{
+  "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
+    },
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "rstrip": false,
+      "single_word": false,
+      "special": false
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+      "rstrip": false,
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