The principle of "garbage in, garbage out" is especially true for fine-tuning. A model's ability to learn a new task is directly constrained by the data you provide. The initial, and perhaps most significant, step in the fine-tuning workflow is to find and validate a source of data that accurately reflects the specific knowledge or behavior you want the model to adopt. This process involves a careful evaluation of potential datasets, whether they are publicly available or from internal, private sources.
Your path to find the right data will lead you to two primary categories of sources: public and private.
Public Datasets are readily accessible and often serve as excellent starting points. They are typically found in research repositories, data-sharing platforms, and open-source projects.
datasets library. You can filter by task (e.g., text generation, summarization), language, and license. Datasets like databricks/dolly-15k (instruction-following) or samsum (dialogue summarization) are popular starting points.Private Datasets are proprietary to an organization. This is often the most valuable data because it is unique to your use case. Examples include:
While private data offers a distinct advantage, it frequently requires more intensive cleaning and structuring, as it was not originally created for machine learning purposes.
Once you identify a potential dataset, you must rigorously evaluate it against several criteria. A weak choice at this stage will create significant problems later, which no amount of hyperparameter tuning can fix.
A decision framework for evaluating a candidate dataset for fine-tuning.
The dataset must be closely aligned with your target domain and the specific task you want the model to perform.
A mismatch in relevance is the most common reason for a fine-tuning project to fail. The model will learn exactly what you show it, so show it what you want it to produce.
High-quality data is clean, consistent, and accurate. Low-quality data introduces noise that can confuse the model, leading to poor performance, hallucinations, or reinforcement of unwanted behaviors. When inspecting a dataset, look for:
<p>, <div>), markdown formatting, or boilerplate text ("Click here to subscribe")?{"question": "...", "answer": "..."} or is the format irregular? Inconsistent labeling or structure will degrade learning.A small, high-quality dataset is almost always better than a massive, noisy one.
A good dataset should contain a diverse range of examples that cover the breadth of your target domain. If you are fine-tuning a model to answer questions about a specific software library, your data should include examples for all major modules, not just the most popular one. A lack of diversity can cause the model to overfit to the few patterns it has seen, making it brittle and unable to generalize to slightly different inputs.
For instance, if all your instruction examples start with "Please explain...", the model might struggle with prompts that begin with "What is...".
A common question is, "How much data do I need?" There is no single answer, as the required amount depends on:
For many instruction-tuning tasks, you can achieve noticeable improvements with as few as a few hundred to a few thousand high-quality examples. For full fine-tuning on a specialized domain, you might need tens of thousands of examples or more. Start with a smaller, high-quality set, evaluate the results, and scale up if necessary.
This is a non-technical but extremely important checkpoint, especially for commercial applications. Datasets are creative works and come with licenses that dictate how they can be used.
Always check the license before investing time in preprocessing a dataset. The Hugging Face Hub conveniently displays the license for each dataset, making this check straightforward. Failure to comply with licensing terms can have serious legal consequences.
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