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Model: RAS1981/qwen3-0.6b-turn-detection-v1 Source: Original Platform
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117
README.md
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README.md
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---
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base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- qwen3
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license: apache-2.0
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language:
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- en
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library_name: transformers
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datasets:
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- RAS1981/turn-detection-probability-balanced
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---
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# 🇷🇺 Qwen3-0.6B Turn Detection (Probability-Based)
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This model is a **specialized conversational boundary detector** for Russian real-estate dialogues.
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It predicts the **probability** that a user has finished their turn (`<|im_end|>`) versus continuing their sentence. It is fine-tuned using **Single-Token Loss Masking** on a balanced dataset of ~20k complete and incomplete conversational turns.
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## 🚀 Key Features
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- **Base Model:** `unsloth/Qwen3-0.6B` (fast, efficient, good Russian support).
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- **Method:** Probability-based Turn Detection. Instead of a binary classifier head, it uses the model's intrinsic next-token prediction.
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- **Performance:**
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- **Complete Turns:** Predicts `<|im_end|>` with high confidence (>90%).
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- **Incomplete Turns:** Predicts the *continuation word* (next token), assigning near-zero probability to `<|im_end|>`.
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- **Latency:** Extremely fast inference on CPU/GPU due to 0.6B size.
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## 📊 Training Data
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Trained on **[RAS1981/turn-detection-probability-balanced](https://huggingface.co/datasets/RAS1981/turn-detection-probability-balanced)**.
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- **Contrastive Pairs:** Each complete sentence has a corresponding incomplete version.
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- **Balanced:** 50% complete turns, 50% incomplete turns.
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- **Domain:** Russian real-estate inquiries (renting, buying, viewing).
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## 🛠️ How to Use (Inference)
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### 1. Load Model & Tokenizer
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```python
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from unsloth import FastLanguageModel
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import torch
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model_name = "RAS1981/qwen3-0.6b-turn-detection-probability-balanced"
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name=model_name,
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max_seq_length=2048,
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dtype=None,
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load_in_4bit=True,
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)
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EOS_ID = tokenizer.eos_token_id # 151645 for Qwen
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```
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### 2. Predict Turn Completion Probability
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The core idea is to check the probability of the **End-of-Sequence (EOS)** token.
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```python
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@torch.no_grad()
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def get_eos_prob(text):
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# Prepare chat template
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messages = [
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{"role": "system", "content": "Ты определяешь конец реплики пользователя по смыслу."},
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{"role": "user", "content": text}
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]
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# Format prompt WITHOUT generation prompt
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
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# Tokenize and STRIP trailing EOS if present (critical step!)
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prompt_ids = tokenizer(prompt, add_special_tokens=False).input_ids
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# Qwen adds <|im_end|>\n automatically. Strip them to predict the boundary.
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if len(prompt_ids) > 2 and prompt_ids[-1] == 198 and prompt_ids[-2] == 151645:
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prompt_ids = prompt_ids[:-2]
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elif len(prompt_ids) > 1 and prompt_ids[-1] == 151645:
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prompt_ids = prompt_ids[:-1]
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inputs = torch.tensor([prompt_ids]).to("cuda")
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# Get logits for the LAST token position
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logits = model(inputs).logits[:, -1, :]
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# Calculate probability of EOS token
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prob = torch.softmax(logits, dim=-1)[0, EOS_ID].item()
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return prob
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# Example Usage
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print(get_eos_prob("До свидания.")) # High Prob (e.g., 0.96) -> Turn Complete
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print(get_eos_prob("Я хотел бы узнать...")) # Low Prob (e.g., 0.00) -> Turn Incomplete
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```
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## 📈 Evaluation Results
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| Phrase | Type | EOS Probability | Interpretation |
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|---|---|---|---|
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| `"До свидания."` | **Complete** | **0.9626** | **CONFIDENT END** |
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| `"Алло, здравствуйте"` | Ambiguous | 0.2599 | WAIT (User likely continues) |
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| `"Я хотел бы узнать про"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
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| `"Нет, вы знаете, я наверное"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
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### Threshold Recommendation
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- **Turn Complete:** `prob > 0.5` (Safe default)
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- **Turn Incomplete:** `prob <= 0.5`
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## 🧠 Methodology: Single-Token Loss Masking
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We trained the model to optimize the loss **only on the final token**.
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- For **complete** examples, the target label is `<|im_end|>`.
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- For **incomplete** examples, the target label is the *actual next word*.
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- All previous tokens are masked with `-100` in the loss function.
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This forces the model to focus purely on the boundary condition: *"Given this context, does the turn end here or continue?"*
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## 📜 License
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Apache 2.0
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29
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|PAD_TOKEN|>": 151669,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set current_content = message.content if message.content is defined and message.content is not none else '' %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = current_content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (current_content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
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{%- set start_pos = 0 %}
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{%- endif %}
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{%- set end_of_message = current_content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set m_content = message.content if message.content is defined and message.content is not none else '' %}
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{%- set content = m_content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in m_content %}
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{%- set content = (m_content.split('</think>')|last).lstrip('\n') %}
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{%- set reasoning_content = (m_content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and (not reasoning_content.strip() == '')) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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61
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"torch_dtype": "float16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 16,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 151669,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.2.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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merges.txt
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merges.txt
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Load Diff
3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:41b617daa741da35bd225a900268c229d0933a9821a470241627c759ece95fce
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size 1192135096
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special_tokens_map.json
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": "<|PAD_TOKEN|>"
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}
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:10ba4ba91270b1a50e5cd8e51023bccc66fc4ac4909dd7ae7ab29433411c9bb9
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size 11422844
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249
tokenizer_config.json
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249
tokenizer_config.json
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1
vocab.json
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1
vocab.json
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