Dr.Hassan Sherwani
Data Analytics and AI Practice Head
December 3, 2024
What distinguishes DBRX AI from the other models is its efficiency—it only uses 36 billion active parameters out of 132 billion, enabling faster inference (reportedly 2x faster than Llama2-70B) and greater flexibility- two factors necessary for companies that want to scale their AI projects.
Introducing Databricks DBRX
Databricks DBRX model is promoted as “transformer-based, decoder-only LLM, with a mixture-of-experts (MoE) architecture, its performance surpassing GPT-3.5 and contending with leading proprietary models like Gemini 1.0 Pro”. But what does this mean to a company that wants to understand the DBRX model better? To decode it, let’s clarify some of the terms used.
- Transformer-based refers to transformer architecture in which the model relies on a mechanism called “self-attention,” which allows it to focus on different parts of the prompt when making predictions, helping it better understand the prompt.
- Decoder-only refers to the model’s ability to use “masked self-attention” to generate text based on the previous context. Unlike encoder-decoder models, where the encoder considers the entire input, the decoder-only model processes input in sequence, making it useful for generative language tasks such as text completion, dialogue, and story generation.
- Mixture-of-experts (MoE) Architecture is an advanced LLM neural network comprising several sub-models with specialized capabilities. Based on the task, this architecture uses a gating network to utilize only the subset of models needed without using excess computational power. Models built with this architecture can be used for text generation, multi-task learning, and other NLP tasks, thus allowing high levels of scalability for the model.
3 Reasons Why DBRX Should be Part of Your Enterprise AI Strategy
High Performance Across Multiple Domains:
In a set of studies conducted, the DBRX model was pitted against several rival open-source LLMs, and it was found that Databricks DBRX outperforms the competition in mathematics, general understanding, and programming. DBRX AI achieved a 70.1% pass rate on the HumanEval benchmark test for programming tasks, outperforming specialized models like CodeLLaMA-70B. This achievement highlights the DBRX model’s usefulness to enterprises that need AI for software development, coding, and NLP tasks.
Efficiency, Scalability, Customizability:
Databricks’ DBRX MoE architecture ensures that companies do not have to trade off regarding model quality and inference speed. By generating up to 150 tokens every second on the Mosaic AI model serving, the DBRX model easily handles large-scale deployments. In recognition of enterprises sometimes needing a specialized LLM, Databricks has developed DBRX Instruct, specializing in tasks like conversational AI, customer service automation, and more due to its ability to excel at specific tasks like instruction following.
Accessibility of Open-Source with Enterprise-Grade Performance
Databricks DBRX is an open-source LLM, with its code available on Hugging Face, allowing developers and companies to control how they use, customize, and scale the model. Businesses can create custom models that are trained on proprietary data to meet their unique use cases and allow them to create IP that organically aligns with their goals. As stated earlier, the model’s performance already outshines several open-source and proprietary LLMs.
Partner with Royal Cyber for a Successful Databricks DBRX Implementation
Our Databricks DBRX Model Services
- Integration and Customization: DBRX AI is an LLM; therefore, successfully leveraging it goes beyond merely acquiring it – it needs to be integrated within workflows. With over a decade of data and AI/ML expertise, our team can help customize this LLM to meet specific needs. We also provide support services for integrating DBRX with other enterprise systems like Databricks’ GenAI-powered products, SQL, and more.
- End-to-End Support: From conducting readiness assessments to deploying AI models, our team provides both consulting and implementation services on how to utilize Databricks tools like Apache Spark™, Unity Catalog for data management, and MLflow for experiment tracking.
- Cost-Efficient Services: Training the DBRX model can be resource-intensive. However, with the MoE architecture and our expertise, we help you significantly reduce the computational cost and time associated with training and inference. Training DBRX is about 2x more efficient computationally than training dense models (LLMs that use parameters for every input) of similar quality. We work with you to optimize resource allocation and performance, ensuring your AI operations are cost-effective and scalable.
- Security, Governance, and Compliance: Implementing LLMs at scale requires careful consideration of security, data governance, and compliance. As a Databricks partner, we are well-versed in using data governance and security tools that ensure your AI models built on Databricks DBRX meet the highest security and compliance standards. Your enterprise can now focus on innovation without worrying about compliance risk.
Harini Krishnamurthy
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