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The One-Stop Shop to Create your GPT

Unlock the full potential of your large language models (LLMs) with Kili Technology's comprehensive fine-tuning platform. We streamline the fine-tuning process, focusing on what truly matters: precision, efficiency, and quality. Spend less time labeling and doing the guesswork, and ship high-value fine-tuned models faster.

The One-Stop Shop to Create your GPT

Mark S.

Enterprise (>1000 employees)

Validated Reviewer Verified User Source: Organic

Suparna T.

Mid-market (51-1000 employees)

Validated Reviewer Verified User Source: Organic

Kickstart LLM Fine-Tuning with Precise Evaluation

Fine-tuning language models start with a comprehensive evaluation. Define custom evaluation metrics tailored to your needs. Rank an LLM's output based on your criteria. Measure creativity, instruction adherence, reasoning, and factuality, and more. Our two-tiered evaluation process combines initial LLM assessments with meticulous human reviews, ensuring unparalleled accuracy and scalability.

Kickstart LLM Fine-Tuning with Precise Evaluation

High-Quality Labeling: Ensure Top-Notch Data Annotation

Fine-tuning LLM represents a new category of annotation projects. It encompasses a new mix of tasks (classification, ranking, and transcription), as well as a new type of asset (dialogue utterances). Kili natively handles this diversity, covering all the needs related to RLHF and supervised fine-tuning. Our advanced QA workflows, scripting capabilities, and error detection mechanisms guarantee data of the highest quality.

High-Quality Labeling: Ensure Top-Notch Data Annotation

Build a Fine-Tuned Model Faster

Combine attention to detail with lightning-fast labeling. Leap ahead with our state-of-the-art automated labeling features. Our user-friendly platform is designed for speed and efficiency, enabling labelers to work faster and more accurately. Customizable workflows and shortcuts reduce the cognitive load, making data annotation a breeze.

Build a Fine-Tuned Model Faster

Feedback Conversion: Transform User Insights Into Actionable Training Data

Leveraging user feedback is essential yet challenging due to its variability and limited informational content. Kili's sophisticated filtering technology empowers you to extract actionable insights from user interactions, enabling targeted annotation efforts and significantly enhancing your LLM's performance.

Feedback Conversion: Transform User Insights Into Actionable Training Data

Seamless Integration with leading LLMs: Eliminate Unnecessary 'Glue' Code

When it comes to LLMs, glue code is the main barrier to implementing a data-centric AI loop. At Kili, we understand this challenge. That's why you can natively use a Copilot LLM-powered system to annotate your fine-tuning projects. Moreover, you can also take advantage of our plug-and-play integrations with market-leading LLMs (e.g., GPT) for fine-tuning.

Seamless Integration with leading LLMs: Eliminate Unnecessary 'Glue' Code

Expert Annotator Access: Engage a Specialized Workforce for Efficient Labeling

Fine-tuning LLMs requires both in-depth industry expertise and professional annotators to ensure quality. At Kili, we offer qualified data labelers with years of experience crafting training datasets. We handpick labelers possessing specific expertise relevant to your industry, ensuring high-quality standards, and deliver your labeled dataset swiftly, within days.

Expert Annotator Access: Engage a Specialized Workforce for Efficient Labeling

Secure your AI Innovations: Uncompromised Security for Fine-tuning LLMs

Data is the new gold. Safeguarding this precious asset through every step of your AI and machine learning projects is paramount. That's why we've committed to the highest standards of data protection, ensuring your projects are not only efficient and effective but also secure and compliant. Kili is ISO 27001 certified and SOC2 certified.

Secure your AI Innovations: Uncompromised Security for Fine-tuning LLMs

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Frequent Questions

What is an Large Language Model fine-tuning tool?

An LLM fine-tuning tool is a platform allowing you to run fine-tuning tasks on large language models (LLMs). A fine-tuning process applied through Kili will allow you to take pre-trained models like ChatGPT and adapt them to your domain-specific data. Adapting models to your use case is done by applying few-shot learning.

What are the tasks for fine-tuning a Large Language Model (LLM)?

When it comes to LLMs, fine-tuning pre-trained models requires performing a set of specific tasks. The training process starts with evaluating the model to see if this specific dataset could be a good basis for your use case. Then you can run tasks such as classification, ranking, and transcription on text and prompts. The fine-tuning process can be done on models like ChatGPT, Bard, or Llama.

What are the terms specific to LLM fine-tuning methods?

There are a few terms that are specific to machine learning and LLM fine-tuning:

  • Multi-task learning: multi-task learning (MTL) is a type of machine learning technique where a model is trained to perform multiple tasks simultaneously. In deep learning, MTL refers to training a neural network to perform multiple tasks by sharing some of the network’s layers and parameters across tasks

  • Parameter-efficient fine tuning: parameter-efficient fine tuning is a method where only a small number of (extra) model parameters are fine-tuned, while freezing most parameters of the pre-trained large language models, thereby greatly decreasing the computational and storage costs in the fine-tuning process.

  • Sequential fine-tuning: sequential fine-tuning is the process of training a pre-trained model on one specific task and subsequently refining it through incremental adjustments.

  • Transfer learning: transfer learning is a technique in machine learning and in the fine-tuning process in which knowledge learned from one task is re-used in order to boost model's performance on other tasks that are related.

  • Model's weights: weights are all the parameters (including trainable and non-trainable) of any model. In many cases such as in the fine-tuning process, this is what you are learning with your system.

  • Low-rank adaptation: low-rank adaptation of large language models (LoRA) is a training method that accelerates the training and fine tuning of large models while consuming less memory. LoRA freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.

Why should I fine-tune an LLM model?

Fine-tuning a large language model like GPT is a powerful way to adapt it for specific tasks or to better align it with particular preferences, requirements or data domains. Fine-tuning an LLM makes it possible to harness the general capabilities of advanced models and tailor them to achieve high performance on more specialized tasks or datasets. It's a critical step for businesses, researches, and developers looking to leverage AI in bespoke applications or to ensure their AI systems align with specific ethical, stylistic, or operational goals.

What is the best LLM? Should I choose open-source or commercial LLMs?

Choosing the "best" large language model depends on several factors, including your specific needs, the nature of your project, budget considerations, and whether you prioritize openness and customizability over turn-key solutions. Both open-sourced and commercial LLMs have their advantages and drawbacks.

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