Airtrain AI

What is Airtrain AI?

Who created Airtrain AI?

Airtrain.AI Llm Playground was founded by Emmanuel Turlay in 2022. The company is focused on building infrastructure for the AI age and offers a no-code platform for data curation, LLM evaluation, and fine-tuning. The team behind Airtrain.AI comprises tech industry veterans with extensive experience in infrastructure development at notable companies like Cruise, Google, Microsoft, and research institutions like CERN and Sandia National Lab.

What is Airtrain AI used for?

• AI Metrics and Evaluation
• AI coding assistants
• Fine-tune and evaluate large language models (LLMs)
• Reduce inference cost by up to 90% by fine-tuning small open-source models on curated datasets
• Customize LLMs to specific use cases
• Vibe-check and compare quality, performance, and cost across a wide selection of open-source and proprietary LLMs
• Accelerate AI workflows with tools for dataset curation, LLM fine-tuning, and evaluation
• Discover semantic clusters and unseen patterns in datasets
• Generate high-quality datasets
• Compare LLMs on an entire evaluation set
• Data analysis tools
• Fine-tune and evaluate LLMs such as Mistral 7B, Llama 3, and Gemma 2
• Reduce inference cost by up to 90% by fine-tuning small open-source models on high-quality curated datasets
• Curate high-quality datasets
• Set up evaluation harness and export models easily
• Discover semantic clusters in data
• Auto-clustering for data curation
• AI labeling/classification
• Explore and curate unstructured datasets

Who is Airtrain AI for?

• Tech industry professionals
• Data scientists
• Engineers

How to use Airtrain AI?

To use the Airtrain.AI LLM Playground, follow these steps:

1. Visit the Airtrain.AI website and access the LLM Playground.
2. Explore a wide range of open-source and proprietary LLMs available for comparison, such as Claude, Gemini, Mistral AI models, Open AI models, Llama 2, Phi-2, and more.
3. Utilize the no-code playground to vibe-check and compare quality, performance, and cost simultaneously across different LLMs.
4. Analyze data using tools for data analysis, AI coding assistants, and AI metrics evaluation.
5. Take advantage of the platform to fine-tune and evaluate LLMs easily without complex infrastructure work.
6. Customize LLMs to your specific use case, improve quality, and reduce costs significantly, as mentioned by satisfied users.
7. Benefit from features like Mistral 7B, Llama 3, and Gemma 2 to fine-tune models, set up evaluation harness, and export the model effortlessly.
8. Accelerate AI workflows by leveraging Airtrain's suite of tools for dataset curation, LLM fine-tuning, and evaluation.

By following these steps, you can effectively utilize the Airtrain.AI LLM Playground for comprehensive LLM analysis and comparison.