Train your own AI models from scratch
Skip the guesswork and accelerate your model training jobs with our field-tested recipes. Each recipe comes with proven architectures, optimized hyper-parameters, and validated training strategies that have already delivered results.
Cost Effective
Save inference cost by deploying small, optimised models that are purpose built from scratch for your use-case
Accurate
Surpass fine-tuned and general purpose models in accuracy by training a model for your specific task
Fast
Smaller, optimised models run magnitutes faster than larger general purpose models
It all starts with data
It all starts with data
First step: Training Data
Just like fine food requires high quality ingridients, training a model from scratch requires high quality data. The Neuralfinity platform allows you to bring your own data, but we also provide you with a michelin-star quality dataset for your model to learn anything, from factual knowledge to writing styles.
Choose your architecture
Choose your architecture
Second Step: Model Architecture
We support a number of open Architectures, such as Llama and GPT, as well as our in-house developed NLM series architecture which features a unique long-context ability that is only limited by available memory at inference time.
Automated model training - anywhere!
Automated model training - anywhere!
Third Step: Compute Orchestration
The Neuralfinity platform automatically specs a cluster for training your model, deploys it to a range of support cloud providers (including on-premise) and manages the training process for you. You can monitor the training process in real-time while we take care of checkpointing, dealing with potential hardware failures and more.
How does it perform?
How does it perform?
Last Step: Evaluate
A model built for your task needs to perform well for your use-case. Our industry leading evaluation tools let you specify exactly how performance is measured and metrics you want to optimise for. We even let you set a baseline through open and commerical models you might be currently using.