Cerebras
AI Model Services
From business strategy to your own
trained state-of-the-art-quality model in weeks
Models that work for you
Cerebras’ Custom AI Model Services pair expert AI strategy with rapid model development to deliver state-of-the-art generative AI solutions for our client. Examples of what we deliver for our customers include:
- Custom Chatbots: Build smart, conversational assistants that can immediately answer nuanced, context-laden questions.
- Code Completion: Leverage intelligent code completion for your programming language in your coding style.
- Summarization: Generate concise and context-savvy summaries of domain-specific documents.
- Classification and NLU: Classify information for more effective text content moderation, sentiment analysis, named entity recognition, and relation extraction.
- Visual Question Answering: Use multi-modal capabilities to answers questions based on images. (Coming soon.)
Jobs we Do
AI Strategy Development
Collaborate with our experts to design an AI strategy that aligns with your business objectives and industry trends. Gain insights on the latest AI advancements and how they can be integrated into your business model.
Accelerate AI Model Design and Build
Our world-class ML experts refine problem definition, process datasets, perform sub-scale experiments to validate the pipeline, train the final model, and perform evaluations to ensure state-of-the-art solutions.
Organizational Upskilling
We educate your team on best practices in training, deploying, and maintaining generative AI models using the ever-familiar PyTorch 2.0 framework on Cerebras AI Supercomputers.
Why Partner with Cerebras
#1
Ranked most published AI Hardware startup in 2023
30+
State-of-the-Art LLMs built with AI supercomputers
50+
PhDs with extensive Machine Learning expertise
1000+
Generative AI Experiments Conducted in 2023
We combine our industry-leading AI supercomputers with a novel training approach to accelerate the development of deployment-ready AI models.
With Cerebras, interconnect bottlenecks are removed, and distributed programming frameworks are not needed. Depending on the size of your model, our simplified approach to training enables us to commit to delivering fine-tuning results within 2 weeks and pre-trained custom models within 2 to 3 months, streamlining the process without compromising quality.
Our industry-leading AI supercomputers ensure you never have to wonder if better accuracy could have been achieved with a bigger model or by training with a larger dataset. Our platform seamlessly scales to large models or datasets. This, in addition to our scaling law expertise, ensures we build the correct size solution for your specific problem.
Training a state-of-the-art model isn’t easy and our team of PhD researchers and ML engineers understand this. We understand the complexities of LLM training. We meticulously prepare experiments and quality assurance checks to ensure a predictable journey to achieving your desired outcomes.
The field of Generative AI is rapidly evolving, with new techniques being published and new models reaching the top of Hugging Face’s leaderboard every day. Partnering with Cerebras ensures that your organization can access the latest advancements and insights, maintaining an edge over the competition. In addition, Cerebras’ world-class AI researchers will help advance AI innovation in your domain.
Accelerated AI Model Design and Build
Model and Data consultation
Co-definition of target quality metrics and baselines to ensure your expectations are met from the start.
Proprietary Vocabulary and Dataset Preparation
Define and acquire training and fine-tuning data, create an optimal vocabulary and tokenizer, tokenize the dataset, and convert it to the optimal format for data ingestion.
Model Definition, Hyper-Parameter Tuning, Scaling Laws Experiments
Conduct small-scale experiments to fit scaling laws to your dataset, define optimal model configurations, and identify hyperparameters for training runs.
Production Model Training Run
Train the initial production model to target accuracy, ready for deployment.
Downstream Fine-Tuning for your Task
Collect quality metrics for the production model on selected tasks and benchmarks.
Evaluation and Model Hand-off
We review evaluation metrics, confirming the model performs as expected, and handoff the final model weights for deployment.
