qwen3-0.6b-turn-detection-v1

🇷🇺 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.

• 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

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.

@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

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