🎲 FiddleCube - Automated dataset generation for fine-tuning LLMs

Create high-quality datasets for fine-tuning and reinforcement learning.

Tl;Dr; Fine-tuning LLMs requires high-quality datasets. FiddleCube automagically generates fine-tuning datasets from your data.

User Data Source > Fine-tuning Datasets (FiddleCube) > Fine-tuning

Head over to fiddlecube.ai to get started!

Hi everyone, we are Neha and Kaushik. We’re building FiddleCube to make high-quality datasets accessible to everyone.

🦸 Kaushik spent most of the last decade building tech at companies like Google, Uber, and LinkedIn.

πŸ§™πŸ» Neha has spent a similar amount of time as a dev at multiple startups, most recently at Uber

πŸ‘«πŸ»πŸ«ΆπŸ» We met at Uber, eventually got married, and decided to build a startup together, following our passion for AI.

😀 The Problem

In the real world, LLMs need to be aligned to follow human instructions. It needs to respond in a manner that is:

  • Positive, Truthful & Honest
  • And in accordance with human beliefs and sensibilities

Remarkable outcomes have been achieved towards this end by fine-tuning and reinforcement learning with high-quality datasets. However, creating these datasets takes significant time, manual effort, and money.

πŸ’‘The Solution

FiddleCube leverages a suite of AI models to create high-quality datasets for fine-tuning and reinforcement learning.

  • Generate annotated datasets from raw data.
  • Augment the datasets - create large datasets to significantly improve model performance.
  • Evaluate and improve the data quality of your training dataset.

We create a rich, diverse, high-quality dataset to produce better models with a lower corpus of data.

βš™οΈ Use Cases


Give the model a personality, voice, and tone. For example, you can create a safe Dora the explorer / Peppa Pig model that speaks to children.

πŸ‘©πŸ»β€πŸ’» API calling and coding

For specific use cases like making API calls or generating code, fine-tuning has provably demonstrated better results. You can fine-tune the LLM on a corpus of code or API data to significantly improve their ability at these tasks.

πŸš„ Increase Throughput, Reduce Latency and Cost

Fine-tuned LLMs are much smaller than the foundational models. You can use them to increase throughput and reduce latency and cost.

πŸ—ΊοΈ Low Resource Domains

LLMs perform poorly in certain domains like vernacular languages. These domains lack a sufficient corpus of high-quality data. Fine-tuning using generated datasets has shown remarkable improvements over the state of the art in these cases.

πŸ™πŸ» Ask

Are you fine-tuning any LLM, or looking to fine-tune LLaMa V2, MPT, or Falcon? We would love to know your use case. Drop a comment on what you are doing, or reach out to us privately!

πŸ‘‹πŸ» Need help with fine-tuning?

Book a slot on our calendar πŸ—“οΈ or drop us a line using:

- Email πŸ“§ : kaushik@fiddlecube.ai

- Typeform πŸ“

and we will get back to you!