Airtrain AI
No-code compute platform for large-scale fine-tuning, evaluation, and comparison of open-source and proprietary Large Language Models (LLMs).
Product Overview
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
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