10 Datasets for Fine-Tuning Large Language Models

ODSC - Open Data Science
9 min readFeb 15, 2024

Large language models have taken the world by storm, offering impressive capabilities in natural language processing. However, while these models are powerful, they can often benefit from fine-tuning or additional training to optimize performance for specific tasks or domains.

In this blog post, we will explore ten valuable datasets that can assist you in fine-tuning or training your LLM. Each dataset offers unique features and can enhance your model’s performance.

Why Fine-Tune a Model?

Fine-tuning a pre-trained LLM allows you to customize the model’s behavior and adapt it to your specific requirements. By exposing the model to domain-specific data, you can improve its performance on tasks related to that domain. Fine-tuning can significantly enhance the model’s accuracy, relevance, and effectiveness for your intended use case.

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Datasets for Fine-Tuning and Training LLMs


The NVIDIA HelpSteer dataset is a collection of 1.4 million human-written instructions for self-driving cars. It covers a wide range of scenarios and includes detailed, step-by-step instructions. This dataset can be valuable for fine-tuning LLMs to generate clear and concise instructions for autonomous vehicles. This is particularly important as clarity and precision in instructions are vital for the safety and reliability of self-driving cars.

By training LLMs with the HelpSteer dataset, it’s possible to enhance the communication interface between the vehicle and its control systems, thereby improving the car’s ability to make informed and accurate decisions in real-time. This integration of LLMs with comprehensive driving data sets marks a significant step towards more intelligent, aware, and responsive autonomous vehicles, potentially revolutionizing the future of transportation.

H2O LLM Studio

H2O LLM Studio is a platform that provides access to a diverse set of datasets for fine-tuning LLMs. It includes datasets from various domains, such as customer service, finance, and healthcare. The platform also offers tools for evaluating and deploying fine-tuned models. These tools are pivotal in ensuring that the LLMs not only understand the nuances of domain-specific language but also perform effectively in practical applications.

The evaluation tools allow users to rigorously test and validate the performance of their models, ensuring they meet the required standards of accuracy and reliability. Once validated, the deployment tools facilitate the integration of these models into real-world applications, be it in automating customer support interactions, analyzing financial documents, or interpreting medical texts. This end-to-end solution provided by H2O LLM Studio not only simplifies the process of utilizing LLMs but also ensures that they can be effectively adapted and applied in a variety of professional contexts, thus broadening the scope of AI applications in industry and beyond.


The No_Robots dataset is a collection of human-written text that excludes any references to robots or artificial intelligence. This dataset can be useful for fine-tuning LLMs to avoid generating responses that are overly technical or robotic. The significance of this lies in its ability to provide a purely human perspective in language, free from the influence of technological or robotic terminology. This characteristic is particularly valuable for fine-tuning LLMs to produce responses that are more natural, relatable, and less technical.

The practical applications of the No_Robots dataset are substantial, especially in areas where human-like interaction is paramount. For instance, in customer service, education, or mental health support, where the quality of interaction can significantly impact user experience, training LLMs with this dataset can enhance the warmth and empathy in AI responses. The removal of technical jargon and robotic references ensures that the language model’s outputs are accessible to a wider audience, regardless of their technical background.

Anthropic HH Golden

The Anthropic HH Golden dataset is a collection of high-quality human-human conversations. It can be valuable for fine-tuning LLMs to generate more natural and engaging responses. It is a curated collection of high-quality human-human conversations, capturing the essence of natural, spontaneous dialogue. The conversations included in the Anthropic HH Golden dataset are diverse, covering a broad range of topics and styles. This variety is crucial as it encapsulates the richness and complexity of human communication, from casual chit-chat to more in-depth discussions. By encompassing different moods, tones, and contexts, this dataset provides a comprehensive resource for understanding the intricacies of human interaction.

Utilizing the Anthropic HH Golden dataset for fine-tuning LLMs offers immense potential to enhance the quality of AI-generated responses. Training LLMs with this dataset can lead to the development of models that are not only proficient in understanding and processing human language but also capable of generating responses that are more natural, relatable, and engaging.

Function Calling Extended

The Function Calling Extended dataset is a collection of code snippets with corresponding function calls. This dataset can be beneficial for fine-tuning LLMs to generate code and improve their understanding of programming concepts. The variety and complexity of these snippets cover a wide range of programming languages and scenarios, offering a detailed perspective on how functions are used and implemented in real-world coding environments. The inclusion of specific function calls alongside the code snippets is particularly beneficial as it provides clear examples of how different functions are invoked, used, and integrated within a larger codebase. This aspect is crucial for understanding the practical application of programming concepts and the logic behind various coding practices.

For fine-tuning in the context of software development, the Function Calling Extended dataset offers significant advantages. By training LLMs with this dataset, these models can develop a deeper and more nuanced understanding of programming concepts and syntax. This is especially important for AI systems designed to assist in coding tasks, where a high level of accuracy and understanding of programming logic is essential.


The DOLMA dataset is a collection of documents and their corresponding logical forms. It can be used to fine-tune LLMs to extract structured data from unstructured text. The essence of the DOLMA dataset lies in its structured approach to unstructured text. Each document in the dataset is accompanied by a logical form that represents the structured, organized version of the information contained within the text. This pairing is invaluable as it demonstrates how unstructured data, often found in natural language texts, can be systematically broken down and translated into a structured format. The dataset covers a wide range of document types and topics, providing a broad spectrum of scenarios for logical data extraction and interpretation.

Utilizing the DOLMA dataset for fine-tuning LLMs opens up new avenues in the realm of information extraction and data structuring. When LLMs are trained with this dataset, they gain the capability to parse and understand complex unstructured text, and then transform it into a structured, logical format. This skill is incredibly useful in numerous applications where large volumes of unstructured data need to be analyzed and organized, such as in legal document analysis, academic research, and business intelligence.


The Open-Platypus dataset is a collection of prompts and corresponding responses designed to evaluate the performance of LLMs on a wide range of tasks. It can be valuable for fine-tuning LLMs to improve their overall capabilities. This dataset is characterized by its diverse compilation of prompts and corresponding responses, specifically curated to assess the performance of LLMs across a broad spectrum of tasks. The range of these prompts is extensive, covering various topics and types of queries, from simple factual questions to more complex, abstract, or creative tasks. The corresponding responses provide a benchmark for how well an LLM can handle these different types of inquiries.

Utilizing the Open-Platypus dataset for fine-tuning LLMs is immensely beneficial in enhancing their overall capabilities. By exposing LLMs to a wide array of prompts and responses during training, they can learn to better understand and respond to a diverse set of inputs. This training can lead to improvements in areas such as context understanding, response accuracy, creativity, and even handling ambiguous or complex questions.


The Puffin dataset is a collection of questions and answers from the popular children’s game “Would You Rather?” It can be used to fine-tune LLMs to generate creative and engaging responses. This game is known for its quirky and often humorous dilemmas, where players choose between two contrasting scenarios. The dataset presents a wide array of imaginative and thought-provoking questions, along with the various answers that people might give. Such content is inherently creative and engaging, often requiring a blend of humor, whimsy, and critical thinking to navigate the choices presented.

Using the Puffin dataset to fine-tune LLMs can significantly boost their ability to generate creative and engaging content. This training can help LLMs to better grasp the nuances of playful, imaginative language and the subtleties of humor, which are often challenging for AI to replicate. Moreover, it can enhance an LLM’s capacity for generating content that is not only accurate and contextually appropriate but also enjoyable and captivating for users. This is particularly valuable in applications where user engagement is key, such as in entertainment, gaming, or interactive learning tools.


The LLaMA-Factory repository provides access to various datasets for fine-tuning and training LLMs. It includes datasets from different domains, such as language modeling, question-answering, and summarization. The repository’s strength lies in its diversity; it encompasses datasets from a variety of domains including language modeling, question-answering, and summarization. This wide-ranging collection is crucial for developing LLMs that are well-rounded and versatile. Language modeling datasets, for instance, are essential for training LLMs to understand and generate coherent, contextually appropriate text.

The integration of these varied datasets from the LLaMA-Factory repository into LLM training regimes can significantly elevate the performance of these models. By fine-tuning LLMs with data from different domains, they can be trained to excel in a wide array of language processing tasks. This multidimensional training approach ensures that LLMs can handle a variety of challenges, from generating natural and engaging dialogue to providing accurate information and summarizing complex documents


The Pile is a massive dataset of text and code, curated by EleutherAI. It can be used to fine-tune LLMs to improve their performance on a wide range of tasks. The sheer size and diversity of this dataset make it a particularly valuable asset for training and fine-tuning Large Language Models. It encompasses a wide array of content, including literary works, academic papers, websites, and programming code, providing a comprehensive spectrum of language styles, formats, and contexts. This diversity is crucial for training LLMs to understand and process a broad range of human language nuances and coding syntaxes.

Utilizing The Pile for fine-tuning LLMs has the potential to significantly enhance their performance across a multitude of tasks. The varied nature of the dataset ensures that LLMs exposed to it can develop a more robust understanding of both natural language and programming languages. This versatility is essential for LLMs to be effective in diverse applications, from text generation and conversation to code writing and debugging.


As you can see, there’s plenty of choice in terms of what direction you want to take when it comes to training your LLM. But if you want to keep up on the latest in large language models, and not be left in the dust, then you don’t want to miss the NLP & LLM track as part of ODSC East this April.

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Confirmed sessions include, with many more to come:

  • NLP with GPT-4 and other LLMs: From Training to Deployment with Hugging Face and PyTorch Lightning
  • Enabling Complex Reasoning and Action with ReAct, LLMs, and LangChain
  • Ben Needs a Friend — An intro to building Large Language Model applications
  • Data Synthesis, Augmentation, and NLP Insights with LLMs
  • Building Using Llama 2
  • Quick Start Guide to Large Language Models
  • LLM Best Practises: Training, Fine-Tuning and Cutting Edge Tricks from Research
  • LLMs Meet Google Cloud: A New Frontier in Big Data Analytics
  • Operationalizing Local LLMs Responsibly for MLOps
  • LangChain on Kubernetes: Cloud-Native LLM Deployment Made Easy & Efficient
  • Tracing In LLM Applications

Originally posted on OpenDataScience.com

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