# Fine Tuning a conversational model

**URL:** <https://discuss.huggingface.co/t/fine-tuning-a-conversational-model/80003>\
**Category:** Beginners\
**Created:** [April 3, 2024, 2:33pm UTC](https://discuss.huggingface.co/t/fine-tuning-a-conversational-model/80003 "2024-04-03T14:33:35Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![anacicconi](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/anacicconi/32/25445_2.png) [@anacicconi](https://discuss.huggingface.co/u/anacicconi)\
**Post date:** [April 3, 2024, 2:33pm UTC](https://discuss.huggingface.co/t/fine-tuning-a-conversational-model/80003/1 "2024-04-03T14:33:35Z")

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Hi, I’d like to know if the format of the dataset and the process to fine tune a model is the same if we are using a model able to keep a conversation (chatgpt style) and one that just answers one shot questions.

I saw many tutorials of developers fine tuning instruction models like T5 to act like a chatbot. If I understood it well, these models will never be able to keep a conversation. They will only answer questions independently. Am I right?

If I want to fine tune a conversational model which one do you advise to play with and is there a tutorial somewhere? I’ve been playing with fine tuning blenderbot small but I couldn’t find any documentation on how the dataset should be or an example of people who did it to check the results.

P.S: I want to fine tune a conversational model to be able to answer questions of a specific domain. Let’s say medicine, for example. I was thinking about using this dataset: [medalpaca/medical\_meadow\_medical\_flashcards · Datasets at Hugging Face](https://huggingface.co/datasets/medalpaca/medical_meadow_medical_flashcards)

Thanks