AI training projects rarely fail at the model level. They struggle when experiments, documentation, and stakeholder updates are scattered across too many tools.
This guide walks you through training models with Databricks DBRX—an LLM that’s up to twice as compute-efficient as other leading models—while keeping the work around it organized in ClickUp.
From setup and fine-tuning to documentation and cross-team updates, you’ll see how a single, converged workspace helps eliminate context sprawl and keeps your team focused on building, not searching. 🛠
How to Use DBRX for AI Model Training
- What is DBRX?
- How to Access and Set Up DBRX
- How to Fine-Tune DBRX and Train Custom AI Models
- DBRX Use Cases for AI Model Training
- How to Integrate DBRX Training With Your Team Workflow
- Common Mistakes to Avoid
- DBRX Vs. Other AI Training Platforms
- Start Training Smarter With ClickUp
- Frequently Asked Questions
Summarize this article for me pleaseWhat is DBRX?
DBRX is a powerful, open-source large language model (LLM) designed specifically for enterprise AI model training and inference. Because it’s open source under the Databricks Open Model License,, your team has full access to the model’s weights and architecture, allowing you to inspect, modify, and deploy it on your own terms.
It comes in two variants: DBRX Base for deep pre-training and DBRX Instruct for out-of-the-box instruction-following tasks.
DBRX architecture and mixture-of-experts design
DBRX solves tasks using a Mixture-of-Experts (MoE) architecture. Unlike traditional large language models that use all of their billions of parameters for every single calculation, DBRX only activates a fraction of its total parameters (the most relevant experts) for any given task.
Think of it as a team of specialized experts; instead of everyone working on every problem, the system intelligently routes each task to the most qualified matching parameters.
Not only does this cut down on response time, but it also delivers top-tier performance and outputs while significantly reducing computational costs.
Here’s a quick look at its key specifications:
- Total parameters: 132B across all experts
- Active parameters: 36B per forward pass
- Expert count: 16 total (MoE Top-4 routing), with 4 active for any given token
- Context window: 32K tokens
DBRX training data and token specifications
The performance of an LLM is only as good as the data it’s trained on. DBRX was pre-trained on a massive 12-trillion-token dataset carefully curated by the Databricks team using their advanced data processing tools. It’s exactly why it performed strongly on industry benchmarks.
Additionally, DBRX features a 32,000-token context window. This is the amount of text the model can consider at one time. A large context window is super helpful for complex tasks like summarizing long reports, digging through lengthy legal documents, or building advanced retrieval-augmented generation (RAG) systems, as it allows the model to maintain context without truncating or forgetting information.
🎥 Watch this video to see how streamlined project coordination can transform your AI training workflow and eliminate the friction of switching between disconnected tools.👇🏽
Summarize this article for me pleaseHow to Access and Set Up DBRX
DBRX offers two primary access routes, both of which provide full access to the model weights under permissive commercial terms. You can use Hugging Face for maximum flexibility or access it directly through Databricks for a more integrated experience.
Access DBRX through Hugging Face
For teams that value flexibility and are already comfortable with the Hugging Face ecosystem, accessing DBRX through the Hub is the ideal path. It allows you to integrate the model into your existing transformers-based workflows.
Here’s how to get started:
- Create or log in to your Hugging Face account
- Navigate to the DBRX model card on the Hub and accept the license terms
- Install the
transformerslibrary along with necessary dependencies likeaccelerate - Use the
AutoModelForCausalLMclass in your Python script to load the DBRX model - Configure your inference pipeline, keeping in mind that DBRX requires significant GPU memory (VRAM) for effective operation
📖 Read More: How to Configure LLM Temperature
Access DBRX through Databricks
If your team already uses Databricks for data engineering or machine learning, accessing DBRX through the platform is the easiest way. It cuts out setup friction and gives you all the tools you need for MLOps right where you’re already working.
Follow these steps within your Databricks workspace to get started:
- Navigate to the Model Garden or the Mosaic AI section
- Select either DBRX Base or DBRX Instruct, depending on your needs
- Configure a serving endpoint for API access or set up a notebook environment for interactive use
- Begin testing inference with sample prompts to ensure everything is working correctly before scaling up your AI model training or deployment
This approach gives you seamless access to tools such as MLflow for experiment tracking and the Unity Catalog for model governance.
📮 ClickUp Insight: The average professional spends 30+ minutes a day searching for work-related information—that’s over 120 hours a year lost to digging through emails, Slack threads, and scattered files.
An intelligent AI assistant embedded in your workspace can change that. Enter ClickUp Brain.
It delivers instant insights and answers by surfacing the right documents, conversations, and task details in seconds—so you can stop searching and start working.
Summarize this article for me pleaseHow to Fine-Tune DBRX and Train Custom AI Models
An off-the-shelf model, no matter how powerful, will never understand the unique nuances of your business. Because DBRX is open source, you can fine-tune it to create a custom model that speaks your company’s language or performs a specific task you would like it to handle.
Here are three common ways you can do this:
1. Fine-tune DBRX with Hugging Face datasets
For teams just starting out or working on common tasks, public datasets from Hugging Face Hub are a great resource. They are pre-formatted and easy to load, meaning you don’t have to spend hours preparing your data.
The process is pretty straightforward:
- Find a dataset on the Hub that matches your task (e.g., instruction-following, summarization)
- Load it using the
datasetslibrary - Ensure the data is formatted into instruction-response pairs
- Configure your training script with hyperparameters like learning rate and batch size
- Launch the training job, making sure to save checkpoints periodically
- Evaluate the fine-tuned model on a held-out validation set to measure improvement
2. Fine-tune DBRX with local datasets
You’ll usually get the best results by fine-tuning with your own proprietary data. This allows you to teach the model your company’s specific terminology, style, and domain knowledge. Just keep in mind that it only pays off if your data is clean and well-prepared, and has sufficient volume.
Follow these steps to prepare your internal data:
- Data collection: Gather high-quality examples from your internal wikis, documents, and databases
- Format conversion: Structure your data into a consistent instruction-response format, often as JSON lines
- Quality filtering: Remove any low-quality, duplicate, or irrelevant examples
- Validation split: Set aside a small portion of your data (typically 10-15%) to evaluate the model’s performance
- Privacy review: Remove or mask any personally identifiable information (PII) or sensitive data
3. Fine-tune DBRX with StreamingDataset
If your dataset ends up being too large to fit into your machine’s memory, no worries, you can use Databricks’ Streaming Dataset library. It allows you to stream data directly from cloud storage while the model is training, rather than loading it all into memory at once.
Here’s how you can do it:
- Data preparation: Clean and structure your training data, then store it in a streamable format like JSONL or CSV in cloud storage
- Streaming format conversion: Convert your dataset into a streaming-friendly format, such as Mosaic Data Shard (MDS), so it can be read efficiently during training
- Training loader setup: Configure your training loader to point to the remote dataset and define a local cache for temporary data storage
- Model initialization: Start the DBRX fine-tuning process using a training framework that supports StreamingDataset, such as LLM Foundry
- Streaming-based training: Run the training job while data is streamed in batches during training, rather than loaded entirely into memory
- Checkpointing and recovery: Resume training seamlessly if a run is interrupted, without duplicating or skipping data
- Evaluation and deployment: Validate the fine-tuned model’s performance and deploy it using your preferred serving or inference setup
💡Pro tip: Instead of building a DBRX training plan from scratch, start with ClickUp’s AI and Machine Learning Projects Roadmap Template and tweak it to your team’s needs. It provides a clear structure for planning datasets, training phases, evaluation, and deployment, so you can focus on organizing your work rather than structuring a workflow.
Summarize this article for me pleaseDBRX Use Cases for AI Model Training
It’s one thing to have a powerful model, but it’s another to know exactly where it shines.
When you don’t have a clear picture of a model’s strengths, it’s easy to spend time and resources trying to make it work where it simply doesn’t fit. This leads to subpar results and frustration.
DBRX’s unique architecture and training data make it exceptionally well-suited for several key enterprise use cases. Knowing these strengths helps you align the model with your business objectives and maximize your return on investment.
Text generation and content creation
DBRX Instruct is finely tuned for following instructions and generating high-quality text. This makes it a powerful tool for automating a wide range of content-related tasks. Its large context window is a significant advantage, enabling it to handle long documents without losing the thread.
You can use it for:
- Technical documentation: Generate and refine product manuals, API references, and user guides
- Marketing content: Draft blog posts, email newsletters, and social media updates
- Report generation: Summarize complex data findings and create concise executive summaries
- Translation and localization: Adapt existing content for new markets and audiences
Code generation and debugging tasks
A significant portion of DBRX’s training data included code, making it a capable LLM support for developers. It can help accelerate development cycles by automating repetitive coding tasks and assisting with complex problem-solving.
Here are a few ways your engineering team can leverage it:
- Code completion: Automatically generate function bodies from comments or docstrings
- Bug detection: Analyze code snippets to identify potential errors or logical flaws
- Code explanation: Translate complex algorithms or legacy code into plain English
- Test generation: Create unit tests based on a function’s signature and expected behavior
RAG and long-context applications
Retrieval-Augmented Generation (RAG) is a powerful technique that grounds a model’s responses in your company’s private data. However, RAG systems often struggle with models that have small context windows, forcing aggressive data chunking that can lose important context. DBRX’s 32K context window makes it an excellent foundation for robust RAG applications.
This lets you build powerful internal tools, such as:
- Enterprise search: Create a chatbot that answers employee questions using your internal knowledge base
- Customer support: Build an agent that generates support responses grounded in your product documentation
- Research assistance: Develop a tool that can synthesize information from hundreds of pages of research papers
- Compliance checking: Automatically verify marketing copy against internal brand guidelines or regulatory documents
Summarize this article for me pleaseHow to Integrate DBRX Training With Your Team Workflow
A successful AI model training project is about more than just code and compute. It’s a collaborative effort involving ML engineers, data scientists, product managers, and stakeholders.
When this collaboration is scattered across Jupyter notebooks, Slack channels, and separate project management tools, you create context sprawl, a situation where critical project info is scattered across too many tools.
ClickUp solves that. Instead of juggling multiple tools, you get one Converged AI Workspace where project management, documentation, and communication live together—so your experiments stay connected from planning to execution to evaluation.
Never lose track of experiments and progress
When running multiple experiments, the hardest part is not training the model; it’s keeping track of what changed during the process. Which dataset version was used, what learning rate performed best, or which run shipped?
ClickUp makes this process super easy for you. You can track each training run separately in ClickUp Tasks, and within tasks, you can use Custom Fields to log:
- Dataset version
- Hyperparameters
- Model variant (DBRX Base vs DBRX Instruct)
- Training status (Queued, Running, Evaluating, Deployed)
That way, every documented experiment is searchable, easy to compare with others, and reproducible.
Keep model documentation tied to work
You don’t have to jump between Jupyter notebooks, README files, or Slack threads to understand the context of an experiment’s task.
With ClickUp Docs, you can keep your model architecture, data prep scripts, or evaluation metrics organized and accessible by documenting them in a searchable doc that links directly to the experiment tasks they came from.
💡Pro Tip: Maintain a living project brief in ClickUp Docs that details every decision, from architecture to deployment, so new team members can always get up to speed with project details without digging through old threads
Give stakeholders visibility in real time
ClickUp Dashboards show experiment progress and team workload in real time. I
nstead of manually compiling updates or sending emails, dashboards update automatically based on the data in your tasks. so stakeholders can check in anytime, see where things stand, and never need to interrupt you with “what’s the status?” questions.
This way, you focus on running experiments rather than constantly having to report on them manually.
Turn AI into your smart project sidekick
You don’t have to manually dig through weeks of training data to get a summary of experiments so far. Just mention @Brain on any task comment, and ClickUp Brain will give you the help you need with full context to your past and ongoing projects.
You can ask Brain to ‘Summarize last week’s experiments in 5 bullet points’ or ‘Draft a doc with the latest hyperparameter results,’ and instantly get a polished output.
🧠 The ClickUp Advantage: ClickUp’s Super Agents take this much further—they can automate entire workflows based on triggers you define, not just answer your questions. With super agents, you can automatically create a new DBRX training task whenever a dataset is uploaded, notify your team, and link relevant Docs when the training run finishes or hits a checkpoint, and generate a weekly progress summary and push it to stakeholders without you touching a thing.
Summarize this article for me pleaseCommon Mistakes to Avoid
Embarking on a DBRX training project is exciting, but a few common pitfalls can derail your progress. Avoiding these mistakes will save you time, money, and a lot of frustration.
- Underestimating hardware requirements: DBRX is powerful, but it’s also large. Attempting to run it on inadequate hardware will lead to out-of-memory errors and failed training jobs. Keep in mind that DBRX (132B) requires at least 264GB of VRAM for 16-bit inference, or roughly 70GB-80GB when using 4-bit quantization
- Skipping data quality checks: Garbage in, garbage out. Fine-tuning on a messy, low-quality dataset will only teach the model to produce messy, low-quality outputs
- Ignoring context length limits: While DBRX’s 32K context window is generous, it’s not infinite. Feeding the model inputs that exceed this limit will result in silent truncation and poor performance
- Using Base when Instruct is appropriate: DBRX Base is a raw, pre-trained model intended for further, large-scale training. For most instruction-following tasks, you should start with DBRX Instruct, which has already been fine-tuned for that purpose
- Siloing training work from project coordination: When your experiment tracking lives in one tool and your project plan in another, you create information silos. Use an integrated platform like ClickUp to keep your technical work and project coordination in sync
- Neglecting evaluation before deployment: A model that performs well on your training data might fail spectacularly in the real world. Always validate your fine-tuned model on a held-out test set before deploying it to production
- Overlooking fine-tuning complexity: Because DBRX is a Mixture-of-Experts model, standard fine-tuning scripts may require specialized libraries like Megatron-LM or PyTorch FSDP to handle parameter sharding across multiple GPUs
Summarize this article for me pleaseDBRX Vs. Other AI Training Platforms
Deciding on an AI training platform involves a fundamental trade-off: control vs. convenience. Proprietary, API-only models are easy to use but lock you into a vendor’s ecosystem.
Open weights models like DBRX offer complete control but require more technical expertise and infrastructure. This choice can leave you feeling stuck, unsure which path actually supports your long-term goals—a challenge many teams face during AI adoption.
This table breaks down the key differences to help you make an informed decision.
| Criteria | DBRX | GPT-5 / GPT-5.2 | LLaMA 3.1 / 4 | Claude 4.5 |
| Weights | Open (Custom) | Proprietary | Open (Custom) | Proprietary |
| Fine-tuning | Full Control | API-based | Full Control | API-based |
| Self-hosting | Yes | No | Yes | No |
| License | DB Open Model | OpenAI Terms | Llama Community | Anthropic Terms |
| Context | 32K | 128K – 1M | 128K | 200K – 1M |
DBRX is the right choice when you need full control over the model, must self-host for security or compliance, or want the flexibility of a permissive commercial license. If you don’t have dedicated GPU infrastructure—or you value speed to market more than deep customization—API-based alternatives may be a better fit.
Summarize this article for me pleaseStart Training Smarter With ClickUp
DBRX gives you an enterprise-ready foundation for building custom AI applications, with the transparency and control you don’t get from proprietary models. Its efficient MoE architecture keeps inference costs down, and its open design makes fine-tuning easy. But strong tech is only half the equation.
True success comes from aligning your technical work with your team’s collaborative workflow. AI model training is a team sport, and keeping experiments, documentation, and stakeholder communication in sync is crucial. When you bring everything into a single converged workspace and cut down on context sprawl, you can ship better models, faster.
Get started for free with ClickUp to coordinate your AI training projects in one workspace. ✨
Summarize this article for me pleaseFrequently Asked Questions
How do I monitor training progress in DBRX?
You can monitor training using standard ML tools like TensorBoard, Weights & Biases, or MLflow. If you’re training within the Databricks ecosystem, MLflow is natively integrated for seamless experiment tracking.
Can I integrate DBRX with CI/CD and deployment pipelines?
Yes, DBRX can be integrated into standard MLOps pipelines. By containerizing the model, you can deploy it using orchestration platforms like Kubeflow or custom CI/CD workflows.
What is the difference between DBRX Base and DBRX Instruct?
DBRX Base is the foundational pre-trained model intended for teams that want to perform domain-specific continued pre-training or deep architectural fine-tuning. DBRX Instruct is a fine-tuned version optimized for following instructions, making it a better starting point for most application development.
How does DBRX compare to GPT-4 for enterprise AI projects?
The main difference is control. DBRX gives you full access to the model weights for deep customization and self-hosting, whereas GPT-4 is an API-only service.
Can you use DBRX for free?
The DBRX model weights are available for free under the Databricks Open Model License. However, you are responsible for the costs of the compute infrastructure required to run or fine-tune the model.
Everything you need to stay organized and get work done.
