初始化项目，由ModelHub XC社区提供模型

Model: RAS1981/qwen3-0.6b-turn-detection-v1
Source: Original Platform

README.md

@@ -0,0 +1,117 @@
+---
+base_model: unsloth/qwen3-0.6b-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3
+license: apache-2.0
+language:
+- en
+library_name: transformers
+datasets:
+- RAS1981/turn-detection-probability-balanced
+---
+
+# 🇷🇺 Qwen3-0.6B Turn Detection (Probability-Based)
+
+This model is a **specialized conversational boundary detector** for Russian real-estate dialogues.
+
+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.
+
+## 🚀 Key Features
+
+- **Base Model:** `unsloth/Qwen3-0.6B` (fast, efficient, good Russian support).
+- **Method:** Probability-based Turn Detection. Instead of a binary classifier head, it uses the model's intrinsic next-token prediction.
+- **Performance:** 
+  - **Complete Turns:** Predicts `<|im_end|>` with high confidence (>90%).
+  - **Incomplete Turns:** Predicts the *continuation word* (next token), assigning near-zero probability to `<|im_end|>`.
+- **Latency:** Extremely fast inference on CPU/GPU due to 0.6B size.
+
+## 📊 Training Data
+
+Trained on **[RAS1981/turn-detection-probability-balanced](https://huggingface.co/datasets/RAS1981/turn-detection-probability-balanced)**.
+- **Contrastive Pairs:** Each complete sentence has a corresponding incomplete version.
+- **Balanced:** 50% complete turns, 50% incomplete turns.
+- **Domain:** Russian real-estate inquiries (renting, buying, viewing).
+
+## 🛠️ How to Use (Inference)
+
+### 1. Load Model & Tokenizer
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model_name = "RAS1981/qwen3-0.6b-turn-detection-probability-balanced"
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=2048,
+    dtype=None,
+    load_in_4bit=True,
+)
+EOS_ID = tokenizer.eos_token_id # 151645 for Qwen
+```
+
+### 2. Predict Turn Completion Probability
+The core idea is to check the probability of the **End-of-Sequence (EOS)** token.
+
+```python
+@torch.no_grad()
+def get_eos_prob(text):
+    # Prepare chat template
+    messages = [
+        {"role": "system", "content": "Ты определяешь конец реплики пользователя по смыслу."},
+        {"role": "user", "content": text}
+    ]
+    
+    # Format prompt WITHOUT generation prompt
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+    
+    # Tokenize and STRIP trailing EOS if present (critical step!)
+    prompt_ids = tokenizer(prompt, add_special_tokens=False).input_ids
+    
+    # Qwen adds <|im_end|>\n automatically. Strip them to predict the boundary.
+    if len(prompt_ids) > 2 and prompt_ids[-1] == 198 and prompt_ids[-2] == 151645:
+        prompt_ids = prompt_ids[:-2]
+    elif len(prompt_ids) > 1 and prompt_ids[-1] == 151645:
+        prompt_ids = prompt_ids[:-1]
+        
+    inputs = torch.tensor([prompt_ids]).to("cuda")
+    
+    # Get logits for the LAST token position
+    logits = model(inputs).logits[:, -1, :]
+    
+    # Calculate probability of EOS token
+    prob = torch.softmax(logits, dim=-1)[0, EOS_ID].item()
+    return prob
+
+# Example Usage
+print(get_eos_prob("До свидания."))          # High Prob (e.g., 0.96) -> Turn Complete
+print(get_eos_prob("Я хотел бы узнать...")) # Low Prob (e.g., 0.00) -> Turn Incomplete
+```
+
+## 📈 Evaluation Results
+
+| Phrase | Type | EOS Probability | Interpretation |
+|---|---|---|---|
+| `"До свидания."` | **Complete** | **0.9626** | **CONFIDENT END** |
+| `"Алло, здравствуйте"` | Ambiguous | 0.2599 | WAIT (User likely continues) |
+| `"Я хотел бы узнать про"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
+| `"Нет, вы знаете, я наверное"` | **Incomplete** | **0.0000** | **CONFIDENT CONTINUE** |
+
+### Threshold Recommendation
+- **Turn Complete:** `prob > 0.5` (Safe default)
+- **Turn Incomplete:** `prob <= 0.5`
+
+## 🧠 Methodology: Single-Token Loss Masking
+
+We trained the model to optimize the loss **only on the final token**.
+- For **complete** examples, the target label is `<|im_end|>`.
+- For **incomplete** examples, the target label is the *actual next word*.
+- All previous tokens are masked with `-100` in the loss function.
+
+This forces the model to focus purely on the boundary condition: *"Given this context, does the turn end here or continue?"*
+
+## 📜 License
+Apache 2.0