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ishiki-labs/llama3.1-8b-ami

sourceHugging Faceupdated 7mo agoView on Hugging Face

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Llama-3.1-8B-AMI: Proactive Response Prediction in Multi-Party Dialogue

LoRA adapter for Llama-3.1-8B-Instruct fine-tuned on the AMI meeting corpus for proactive response prediction in multi-party conversations. Given a conversational context and a current utterance, the model predicts whether a target speaker will SPEAK next or remain SILENT.

Model Details

—Model type: LoRA adapter for causal language model (text classification / sequence classification)
—Language(s): English
—License: Apache 2.0
—Finetuned from: meta-llama/Llama-3.1-8B-Instruct
—AMI Corpus: Meeting recordings and transcripts: AMI Corpus

Model Sources

—Base model: meta-llama/Llama-3.1-8B-Instruct

How to Get Started with the Model

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "kraken07/llama3.1-8b-ami")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

# Your input format should match training: context turns + current turn
# Output: SPEAK or SILENT prediction for the target speaker

Citation

If you use this model, please cite our work:

bibtex

@misc{bhagtani2026speakstaysilentcontextaware,
  title={Speak or Stay Silent: Context-Aware Turn-Taking in Multi-Party Dialogue},
  author={Bhagtani, Kratika and Anand, Mrinal and Xu, Yu Chen and Yadav, Amit Kumar Singh},
  year={2026},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2603.11409}
}