[WIP/Do Not Review]Chatbot Recipe #403
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richardliaw
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Apr 3, 2024
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| - **Navigating Dataset Limitations**: The perfect dataset for a specific task may not exist. Be mindful of the limitations when choosing from available resources, and understand the potential impact on your project. | ||
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| #### **Best Practices for FineTuning Data Preparation** |
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Generally this is a bit high level; I think a more concrete walkthrough would be much more useful
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| Gathering a diverse and comprehensive dataset is crucial. This dataset should include a wide range of topics and conversational styles to ensure the model can handle various subjects. A recent [research](https://arxiv.org/pdf/2305.11206.pdf) shows that quality of data has far more importance than quantity. Here are some high level thoughts on data collection and preprocessing along with best practices: | ||
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| **NOTE** data collection and processing is very use-case specific and here we can only share best practices but it would be very nuanced for each use-case. |
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Prefer a more specific use case -- like what does "diversity" mean?
| **Tools** | ||
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| - [wimbd](https://github.com/allenai/wimbd) for data analysis. | ||
| - TBD |
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| **Data Decontamination** | ||
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| The process involves eliminating evaluation data from the training dataset. This crucial preprocessing step maintains the accuracy of model evaluation, guaranteeing that performance metrics are trustworthy and not skewed. |
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how do you actually do this in practice? is it like data dedup?
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| ##### Low Quality Dataset | ||
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| Below are some examples of real test on the fine-tuned model with very poor results. It seems fine-tuned model does not show any promising results with this dataset. Looking at the dataset, we could observe that the amount of data (Q&A pair) for each concept such as PyTorch FSDP and Llama-Recipe is very limited and almost one pair per concept. This shows lack of relevant training data. The recent research showed that from each taxonomy having 2-3 examples can yield promising results. |
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what makes the data low-quality?
how do i detect this for my own data?
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| # Please implement your own chat service class here. | ||
| # The class should inherit from the ChatService class and implement the execute_chat_request_async method. | ||
| class OctoAIChatService(ChatService): |
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maybe just use one of those generic openai clients like litellm?
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Main Goal
Building an e2e recipe for building chatbots.
High level idea
We want to focus on following stages:
Use-case