56 lines
3.2 KiB
Markdown
56 lines
3.2 KiB
Markdown
---
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license: apache-2.0
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base_model:
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- HuggingFaceTB/SmolLM2-1.7B-Instruct
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language:
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- en
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pipeline_tag: text-generation
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datasets:
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- zenglhardt/convfill-dataset
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---
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# Model Card for SmolLM1.7B-IT-ConvFill
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This model card implements a finetune of [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) for the conversational infill task described in the [ConvFill paper](https://arxiv.org/abs/2511.07397).
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## Model Details
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This model should be used respecting the original license of the base model, HuggingFaceTB/SmolLM2-1.7B-Instruct. The dataset that was used to finetune this model can be found [here](https://huggingface.co/datasets/zenglhardt/convfill-dataset).
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### Model Description
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Deploying responsive, multi-turn conversational voice agents with large language models poses a critical challenge: cloud-based foundation models utilize reasoning, information retrieval, and tool use for high-value tasks, but introduce latency that disrupts natural conversation. In contrast, small models can respond quickly but lack capabilities needed in real-world tasks. We propose _conversational infill_, a task where a small, local model generates prompt, contextually appropriate dialogue and seamlessly incorporates delayed, external knowledge produced in parallel by a foundation model backend. This finetune trains HuggingFaceTB/SmolLM2-1.7B-Instruct to perform the conversational infill task.
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- **Finetuned from model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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### Model Sources [optional]
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- **Repository:** https://github.com/vysri/conversational-infill
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- **Paper:** [Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents](https://arxiv.org/abs/2511.07397)
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- **Demo:** TBD
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### Direct Use
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This model is intended to be used with the infrastructure in the [ConvFill repository](https://github.com/vysri/conversational-infill).
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## Bias, Risks, and Limitations
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This model is not explicitly tuned for guardrailed behavior. Please use with caution.
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## How to Get Started with the Model
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Use the code in the [ConvFill repository](https://github.com/vysri/conversational-infill) to get started with this model.
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### Training Data
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A link to the training data for this model can be found [here](https://huggingface.co/datasets/zenglhardt/convfill-dataset). The dataset generation procedure can be found [here](https://github.com/zenglhardt/convfill-dataset). Information on training procedures can be found in the [ConvFill paper](https://arxiv.org/abs/2511.07397). Training code and scripts can be found in the [ConvFill repository](https://github.com/vysri/conversational-infill).
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## Citation
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```
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@misc{srinivas2026thinkingspeakinginferencetimeknowledge,
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title={Thinking While Speaking: Inference-Time Knowledge Transfer for Responsive and Intelligent Conversational Voice Agents},
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author={Vidya Srinivas and Zachary Englhardt and Vikram Iyer and Shwetak Patel},
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year={2026},
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eprint={2511.07397},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2511.07397},
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}
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``` |