The One-Stop Shop to Create your GPT
For successful LLM fine-tuning, five elements are crucial: clear evaluation, high-quality data labeling, turning user feedback into high-quality training data, seamless LLM integration, and expert annotator access. Kili Technology encapsulates all of that in an all-in-one solution.
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Clear Evaluation: Assess Whether Fine-Tuning is a Viable Option
Evaluating LLMs is key to determining when and how much to fine-tune. However, assessing LLMs on generative tasks is challenging. Traditional metrics like BLEU and ROUGE fall short. Kili implements the state-of-the-art solution: your establish custom evaluation criteria (e.g., following instructions, creativity, reasoning, factuality), then LLM does a first assessment followed by a human review – to achieve both scalability and precision.
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. More than that, as we know that labeling at top quality is hard, you can set up advanced QA workflows and implement QA scripts and error detection in ML datasets.
Feedback Conversion: Transform User Insights Into Actionable Training Data
User feedback is a valuable indicator for improving your LLM. However, it has two limitations: it is noisy and often contains too little information to effectively enhance your LLM. On the other hand, thanks to Kili's advanced filtering system, you can swiftly identify significant conversations and thus target your annotation efforts efficiently.
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.
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.
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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.
