Description
Miniclay AI provides an AI fine-tuning engine designed to train custom models on specific datasets. The platform allows users to describe a task and upload labeled examples, such as CSV files for text tasks or images with labels for vision tasks. Miniclay AI then automates the complex process of selecting a base model, checking the dataset for issues like mislabeled rows or class imbalance, and applying the optimal training recipe. This approach ensures that the resulting model is highly specialized for the intended task, often outperforming larger general models on narrow jobs, delivering faster responses, and costing less per call.
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A core benefit of Miniclay AI is the complete ownership of the trained model weights. Users can download, self-host, or push their adapter to HuggingFace, with no lock-in or runtime dependency on Miniclay AI servers. This facilitates private AI deployment, allowing models to run within a user's VPC, datacenter, or even in fully air-gapped environments, ensuring no external API calls or data retention by Miniclay AI after handover.
The platform emphasizes a "measure first, train second" philosophy. Before any training begins, Miniclay AI benchmarks a stock model with retrieval and prompting against the user's task. This provides a baseline score, and if prompting alone meets the user's requirements, no training is recommended or charged. If the baseline falls short, users gain a clear understanding of the performance lift they can expect from fine-tuning.
Miniclay AI offers a transparent, pay-as-you-go pricing model, with the first demo run being free and requiring no credit card. Subsequent training runs are priced per job, and the exact cost is quoted and approved by the user before training commences. This makes fine-tuning accessible even for those without deep machine learning expertise, as the platform handles hyperparameters and evaluation, delivering a ready-to-deploy, scored model.
- CategoryMachine Learning Platforms
- Pricing ModelPay-as-you-go
- DeploymentCloud, Self-hosted
- PlatformsWeb
Miniclay AI's Core Features
AI-powered fine-tuning engine
Custom model training on user data
Automated base model selection
Dataset checking and cleaning (SynthFix)
Baseline benchmarking against stock models
User ownership of trained model weights
Private AI deployment options (VPC, datacenter, air-gapped)
No external API calls during inference
No data retention after handover
Pay-as-you-go pricing per job
Free first demo run without credit card
Pre-training cost approval
API for automated fine-tuning and deployment
Support for text and vision tasks (CSV, images with labels)
LoRA/QLoRA on A10G/A100 for training
How to use Miniclay AI?
Upload Labeled Examples: Provide your labeled data in CSV format for text tasks or images with labels for vision tasks.
Dataset Check: Miniclay AI automatically checks your dataset for mislabeled rows, contradictory examples, and class imbalance.
Benchmark Baseline: A stock model with retrieval and prompting is scored against your task to establish a performance baseline.
Train and Score: If the baseline is not met, Miniclay AI selects a base model, determines hyperparameters, trains, and scores the model.
Approve Cost: Review and approve the exact quoted cost before any paid training job starts.
Take the Weights: Download the trained adapter, self-host it, or push it to your HuggingFace account.
Miniclay AI's Use Cases
- Ticket and Email Routing
- Document Field Extraction
- Data Quality Triage
- Private AI Deployment
- Task-Specific Model Creation
- Cost-Effective Inference
- Security and Compliance
