File size: 1,869 Bytes
3ddb50c 8f8fd12 3ddb50c 8f8fd12 3ddb50c 8f8fd12 3ddb50c 8f8fd12 5e59196 3ddb50c 8f8fd12 3ddb50c 8f8fd12 3ddb50c 8f8fd12 3ddb50c 8f8fd12 3ddb50c 8f8fd12 173460f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | ---
base_model: Qwen/Qwen3-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-8B
- lora
- transformers
- turn-taking
- multi-party-dialogue
- ami
- text-classification
---
# Qwen3-8B-AMI: Proactive Response Prediction in Multi-Party Dialogue
LoRA adapter for **Qwen/Qwen3-8B** 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:** [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
- **AMI Corpus:** Meeting recordings and transcripts: [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/)
### Model Sources
- **Base model:** [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
## How to Get Started with the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "kraken07/qwen3-8b-ami")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")
# 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}
}
``` |