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Verdict

A quick read before you dig into the full page.

Best if
No-code platform for fine-tuning small language models
Pick this when
No-code platform for fine-tuning small language models

Overview

Radal is a no-code platform to fine-tune small language models using your own data. It allows users to connect datasets, configure training visually, and deploy models in minutes. The platform is built for startups, researchers, and enterprises needing custom AI without MLOps complexity.

Radal screenshot

Access & connections

Where you can use Radal.

Platforms

Web

Capabilities

What this tool accepts, produces, and is strongest at.

Highlights

  • No-code visual training flow
  • AI Copilot for tailored flow construction
  • Hugging Face integration for auto-push
  • Export quantized models for local/edge deployment
  • One-click training and visual iteration

FAQ 8

What is Radal?
Radal is a no-code platform to fine-tune small language models using your own data. It allows users to connect datasets, configure training visually, and deploy models in minutes. The platform is built for startups, researchers, and enterprises needing custom AI without MLOps complexity.
How to use Radal?
Radal simplifies the process of training small language models through a visual, no-code interface. Users can connect datasets, drag and drop elements to configure training flows, and interact with an AI Copilot. Models can be trained with one click, iterated visually, and deployed quickly, even on edge devices.
What size models can I train with Radal?
The website lists examples of fine-tuning 3-B, 7-B, 5-B, and 4-B models, indicating support for various small language model sizes. Specific limits are not detailed.
Can I use my own base model?
The provided content does not explicitly state whether users can bring their own base models, but it focuses on fine-tuning existing small language models.
What does the Copilot actually do?
The Interactive AI Layer, or AI Copilot, helps users by constructing a tailored training flow for them.
How can I try out my trained model?
Trained models can be tried out locally and can run on edge-devices for continued inference. The website also mentions rapid deployment workflows for LM Studio and PocketPal.
Why even build this? Don’t LLMs already do everything?
The website emphasizes the future of Small Language Models (SLMs) and provides use cases where domain-specific small models can outperform general-purpose giants, such as a fine-tuned LLaMA-8B beating GPT-4 in biomedical questions.
When can I try it out?
Radal is currently in Beta (v0) and encourages users to 'Try Now' or 'Join the waitlist'.
Radal
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