Turn Detection Model (LLM-base)
Collection
An LLM-based turn detection model optimized for conversational AI systems, enabling accurate real-time identification of user and agent turns in strea
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5 items
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Updated
This model is a fine-tuned version of facebook/MobileLLM-R1-140M. It has been trained using TRL.
from transformers import pipeline
question = """
You are a speaking turn-ending identifier. Your task is to identify whether the user's speaking turn is complete or not. Respond with `end` if the user's turn is complete, or `continue` if it is not.
User input: I want to
"""
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=1, return_full_text=False)[0]
print(output["generated_text"]) # "end" or "continue"
This model was trained with SFT.
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
Base model
facebook/MobileLLM-R1-140M-base