Airtrain AI

What is Airtrain AI?

Airtrain AI is a user-friendly no-code platform designed to streamline the process of fine-tuning, evaluating, and comparing Large Language Models at scale. It enables AI developers, data scientists, and businesses to customize open-source and proprietary LLMs such as GPT-3.5, GPT-4, Claude, Gemini, and Llama 2 using their own datasets without requiring programming skills. The platform supports large dataset exploration, semantic clustering, batch evaluation with multiple metrics, and model fine-tuning, helping users reduce AI costs by up to 90% while tailoring models to specific domain needs. Airtrain AI also integrates with popular frameworks like LlamaIndex and offers a playground for prompt testing and model response comparison.

Key Features

• No-Code Interface

  Enables users without programming experience to upload datasets, fine-tune models, and evaluate LLMs through an intuitive web UI.

• Multi-Model Support

  Supports a wide range of open-source and proprietary LLMs including GPT-3.5, GPT-4, Claude, Gemini, Mistral, Llama 2, and custom models.

• Dataset Exploration and Semantic Clustering

  Provides tools for automatic data segmentation, visualization, and insight generation to curate high-quality datasets.

• Batch Evaluation with Advanced Metrics

  Allows large-scale offline evaluation of multiple LLMs using customizable metrics such as AI scoring, JSON schema validation, and reference-based metrics.

• Fine-Tuning and Model Export

  Facilitates fine-tuning of LLMs on user data with exportable model weights compatible with Hugging Face for deployment.

• LLM Playground

  Interactive environment to test prompts and compare responses from different models side-by-side.

Use Cases

• Custom AI Model Development : Businesses and developers can create tailored LLMs fine-tuned on proprietary data to meet specific domain requirements.
• Cost-Effective AI Solutions : Reduce reliance on expensive proprietary APIs by fine-tuning open-source models, potentially lowering AI costs by up to 90%.
• Large Dataset Curation and Analysis : Data teams can explore, segment, and curate unstructured datasets efficiently to improve training data quality.
• Model Evaluation and Benchmarking : Evaluate and compare multiple LLMs on custom datasets using diverse metrics to select the best model for deployment.
• Prompt Engineering and Testing : Use the playground to experiment with prompts and analyze model outputs for improved AI application performance.

FAQs