Train, host and use multi-modal AI

We help you train, deploy and use AI multi-modal models

Why Choose Us?

We help with all stages of the AI lifecycle.

01

Training

Pre-train and fine tune massive models, tailored to your data and task. Own your weights.
02

Inference

Seamlessly deploy models to your cloud or on-premise solution.
03

Post-Training

RLHF, Agent systems, Chatbots, Document QA, Knowledge Extraction.

Multi-Modal

The future of AI is multi-modal. Lightning fast inference with the ability to query images and text.

Query what's happening in an image using a model trained on your own data
LLM and Image understanding in one model
Host your multi-modal model on premise
Stay ahead of the capability curve
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Large Language Models

Build your own LLM

Custom pre-training, fine-tuning, and RLHF on your propriety data hosted in your secure environment.
Our team has worked on >40B parameter models
Train multi-billion parameter models on 1000s of GPUs
Build your own model, keep the weights, ensure compliance with regulatory requirements
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Image+Video

Train image models for your specific use case. Stay on the cutting edge of video generation.

Our team trained ruDalle (2021) and Kandinsky (2023)
Pre-train and fine tune Stable Diffusion like models to suit your needs
Generate and analyze video data
Create AI avatars
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Audio + Voice

Get on our wavelength

A huge wave of voice and audio AI is coming. Don't miss it!

Voice cloning, text-to-speech, noise reduction.
Custom audio models for your use-case
Music and audio generation
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What We Do?

We provide an end-to-end model training and inference solution.

More Details

Cloud agnostic training orchestrator

Our MLOps team has experience training models on AWS, GCP, and Azure or on-premise orchestration of training jobs.

Optimized Model Training

Hyper-optimized training speedups decrease costs and dev cycle​​. Pre-training, post-training, fine-tuning, SFT, RLHF, DPO, and other algorithms. Automated evaluation your model on all of the latest benchmarks.

Advanced features such as layer freezing, blurpool, cutout, ghost norm, etc

Autoscaling Inference

Model inference speedups improve performance and decrease costs. Our MLOps team can help you serve your model on an autoscaling inference servive

Distillation, pruning, quantization, sparse networks, mixtures of experts, etc

Happy Customers

What customers are saying about us

“Eventum allowed us to be on the cutting edge of text-to-image fine tuning and model serving, helping us get to market quickly and effectively in the consumer app space where speed without technical debt is the name of the game"
Eventum helped us go from stuck to cutting edge in a matter of months. They refreshed our training and MLOps pipelines, allowing us to do high throughput experimentation and use self supervised pre-training to take advantage of all of our data.
"Eventum came in with tremendous speed and expertise to build an MLOps platform from scratch surpassing industry standards, allowing our ML Scientists to work twice as effective. Eventum builds quickly and knows ML thoroughly — they have my highest respect."

Can you use my cloud credits?

Yes!  We can accrue GPUs as they become available, and add them to your training run, increasing efficiency as it scales

Can you develop a custom architecture?

Yes, we can make custom architectures, for example in voice cloning. We also offer advanced methods in model distillation to make significant improvements to inference time.

Can you help with post-training or using LLMs in production?

Yes, we have clients which are using LLMs for many applications - customer service agents, resume screeners, financial data analysis, etc.

Can you keep my data, model and training on-premise?

Yes, we generally prefer to work with Kubernetes based systems, but can make custom tailored training and inference solutions for other ones as well