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Vinkius

Predibase (LLM Serving & Finetuning) Connector for AI agents.

7 live capabilities

Deploy and query fine-tuned models for production ML workflows.

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AI Agent

Why people use Predibase (LLM Serving & Finetuning)

Predibase for Managing Fine-Tuned LLM Deployments

This Connector changes that by bringing your entire Predibase production environment into your AI client. Instead of hunting for a specific endpoint or checking if a deployment is healthy, you just ask your agent to do it. It handles the nuances of LoRA adapter management and inference tasks so you can stay in your flow.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

That you get a direct line to your production models without leaving your chat interface.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Monitoring production model health

    An MLOps engineer asks the agent to check if the customer support model is still online.

  2. Real-world use case 02

    Batch processing of customer reviews

    A data scientist needs to categorize 50 reviews.

  3. Real-world use case 03

    Verifying model configurations

    A developer needs to know which version of a model is running.

Complete set · 7capabilities

The complete Predibase (LLM Serving & Finetuning) capability set.

These are the exact actions your AI can choose when you ask it to work with Predibase (LLM Serving & Finetuning).

Capability set01 / 02

01—04

4 capabilities in this set.

Part of 7 available through Predibase (LLM Serving & Finetuning).

  1. 01 Capability

    Completion

    Create a standard completion for your model to fill in text. Use this when you need a predictable response from a specific fine-tuned model.

  2. 02 Capability

    Chat completion

    Create a chat-style response that follows a conversational format. This is perfect for building interactive bots or customer service interfaces.

  3. 03 Capability

    Classify

    Run batch classification on a list of inputs to group data into categories. This saves time when you need to process large amounts of text at once.

  4. 04 Capability

    Get health

    Check if your inference endpoint is online and responding. Use this to ensure your production models are reachable before starting a task.

Capability set02 / 02

05—07

3 capabilities in this set.

Part of 7 available through Predibase (LLM Serving & Finetuning).

  1. 05 Capability

    Get info

    Get the technical details and metadata for a specific deployment. This helps you verify which model version or configuration you are currently using.

  2. 06 Capability

    Get metrics

    Pull Prometheus metrics to see how your model is performing in real time. It's the best way to monitor request volume and latency for your production apps.

  3. 07 Capability

    Generate text

    Generate text from a specific model deployment to get raw content. This is useful for quick content generation or testing model outputs.

Set up in minutes

One URL. Then ask Predibase (LLM Serving & Finetuning) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Predibase (LLM Serving & Finetuning) from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_anUwoOMv2E6QQVudvBXsDytO30kiB1fpaXuzKUbp/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Predibase (LLM Serving & Finetuning), and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Predibase (LLM Serving & Finetuning) for the conversation.

Where the request belongs

Work Predibase can move forward.

Built around the request

This is for the AI engineer who is tired of jumping between three different dashboards just to see if their fine-tuned model is actually healthy or producing the right JSON.

01

AI Engineer

Tests and validates fine-tuned models in real-time chat sessions to see how they handle specific prompts.

02

Data Scientist

Monitors production inference metrics and checks model health without needing to write custom scripts.

03

MLOps Engineer

Manages LoRA adapters and production deployments across multiple projects from a single interface.

04

ML Developer

Integrates structured LLM outputs into software applications using enforced JSON schemas.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

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01 7 capabilities

Together AI

Generate code, evaluate embeddings, and deploy open-source LLMs instantly from your local agent via Together AI's infrastructure.

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02 10 capabilities

Metatext

No-code NLP and AI model management via Metatext. run inference and manage datasets.

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03 3 capabilities

LangSmith

Observability and evaluation platform for LLM applications. monitor traces, debug agent runs, and track performance metrics across your AI stack.

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04 10 capabilities

Forefront

Access Forefront AI models directly from your agent. generate chat completions, manage fine-tuning jobs, and collect LLM outputs with pipelines.

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05 11 capabilities

Keywords AI

Monitor and optimize your LLM API usage with a unified gateway that tracks costs, latency, and model performance across providers.

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Langfuse (LLM Tracing & Evals) logo
06 10 capabilities

Langfuse (LLM Tracing & Evals)

Monitor LLM apps via Langfuse. track traces, manage prompt templates, and audit evaluation scores.

View Connector

Bring your own AI

Change the model, client or framework. Keep Predibase connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
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  • Zed
  • Continue
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Before you connect

Questions about Predibase.

The practical details behind the request, access and result.

Can I use Predibase MCP to manage my fine-tuned models?

Yes, you can manage your fine-tuned model deployments and LoRA adapters directly through your AI agent. This allows you to switch between models and check their status without leaving your chat interface.

How does Predibase MCP help with production LLMs?

It provides a direct connection to your live production models. You can run inference, check health, and pull real-time metrics to ensure your models are performing correctly.

Can I get structured JSON from my models using Predibase MCP?

Yes, the Connector supports enforcing JSON schemas on model responses. This ensures that your AI agent always receives the structured data your application needs to function.

Does Predibase MCP support LoRA adapters?

It does. You can dynamically apply different LoRA adapters during your inference tasks, allowing you to swap specific model behaviors on the fly.

Can I check my model's performance with Predibase MCP?

Yes, you can pull Prometheus metrics for your deployments. This gives you a clear view of latency, request volume, and other key performance indicators.

Is Predibase MCP good for batch text classification?

Yes, it includes a capability specifically for batch classification. This allows you to process multiple text inputs at once rather than sending them one by one.

Can I use my fine-tuned adapters with this server?

Yes. When using the generate_text capability, you can provide an adapter_id to apply your specific fine-tuned LoRA adapter to the base model deployment.

How do I monitor the performance of my Predibase deployment?

Use the get_metrics capability to scrape Prometheus-formatted metrics or get_info to retrieve metadata like model ID and device type.

Does this support structured JSON responses?

Absolutely. The generate_text capability includes a schema parameter that allows you to pass a JSON schema to ensure the model output follows a specific structure.

One connection away

Give your agent a direct line to Predibase.

Connect Predibase once. Keep it beside 5,900+ managed Connectors when the next task needs more.

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