Turn Detection with anonymguy/turn-detection-cocalai-vllm

This model, developed by anonymguy, is a 0.8 billion parameter Qwen3-based language model specifically fine-tuned for turn detection in conversational AI. It leverages the unsloth/Qwen3-0.6B-Base as its foundation and was trained for efficiency using Unsloth and Huggingface's TRL library, resulting in 2x faster training.

Key Capabilities

• High Accuracy: Achieves an impressive 96.22% accuracy in detecting conversational turns.
• Specialized Training: Fine-tuned on a synthetic dataset of approximately 140 samples, incorporating specific instructions like "Wait." and customer support dialogue, in addition to the turns-2k dataset.
• Efficient Development: Benefits from Unsloth's optimization for faster training.

Good for

• Dialogue Segmentation: Ideal for applications requiring precise identification of speaker turns in conversations.
• Customer Support Automation: Can enhance systems that need to understand and process customer interactions by accurately segmenting dialogue.
• Conversational AI Development: Useful for developers building chatbots, virtual assistants, or transcription services where turn-taking is critical.