anonymguy/turn-detection-cocalai-vllm
The anonymguy/turn-detection-cocalai-vllm is a 0.8 billion parameter Qwen3-based model, fine-tuned by anonymguy for turn detection in conversational AI. It was trained using Unsloth and Huggingface's TRL library on a synthetic dataset including customer support data and the turns-2k dataset, achieving 96.22% accuracy. This model is optimized for identifying conversational turns, making it suitable for applications requiring precise dialogue segmentation.
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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.