Fine-Tune AI Models Using Connectors.
GPT-4 costs $30 per 1M tokens for your classification task , fine-tune a $0.20/M model on Together AI that scores 96% accuracy, track every experiment in W&B, and save $29.80 per million tokens
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your agent identifies the task: 'Classify support tickets into 12 categories.' Step 1: Together AI launches a fine-tuning job on LLaMA-3-8B using your labeled dataset.
Step 2: W&B tracks the training run , loss curves, validation accuracy per epoch, hyperparameter configurations. Step 3: After training, the agent runs inference on your test set and compares against GPT-4 baseline.
Google Sheets gets the ROI report: 'Fine-tuned LLaMA-3-8B: 96.2% accuracy at $0.20/M tokens. GPT-4: 97.1% accuracy at $30/M tokens. Delta: -0.9% accuracy, -99.3% cost.
At 500K classifications/month: $15,000 $100. Annual savings: $178,800.'
Connector Orchestration: 3 Connectors, one intelligent agent
Connect Together AI, Weights & Biases and Google Sheets so your AI agent launches fine-tuning jobs on Together AI, monitors training with W&B experiment tracking, and reports cost-accuracy comparisons in Sheets.
Together Ai
triggerLaunches fine-tuning jobs on open-source models and runs inference for evaluation
create_finetune_job list_finetune_jobs list_available_models chat_completion generate_embeddings Weights Biases
enrichmentTracks fine-tuning experiments , loss curves, hyperparameters, model versions and comparison across runs
list_wandb_projects list_project_runs get_run_details list_project_sweeps list_project_artifacts Google Sheets
actionCost-accuracy comparison dashboard , GPT-4 vs fine-tuned model ROI analysis
create_spreadsheet update_sheet_values append_sheet_values get_sheet_values Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Together Ai, Weights Biases & Google Sheets ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
AI engineers replacing expensive GPT-4 API calls with fine-tuned open-source models at 99% cost reduction
Startups tracking fine-tuning experiments with W&B to find the optimal accuracy-cost trade-off
ML teams building cost-accuracy comparison dashboards to justify fine-tuning ROI to leadership
AI enthusiasts learning model fine-tuning hands-on with managed infrastructure
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need?
Three: Together AI, Weights & Biases and Google Sheets.
Does this work with Claude Desktop?
Yes. Any MCP-compatible AI client works.
Do I need GPUs for fine-tuning?
No. Together AI manages all GPU infrastructure.
Is my training data secure?
Connectors authenticate via API keys. Data stays in your Together AI and W&B accounts.
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Connectors used in this workflow
Together AI
Together AI MCP lets you run open-source models like Llama and Mixtral directly through your AI agent. You can use it to generate text, create embeddings for vector databases, fine-tune models on your own datasets, and even generate images from text prompts. It connects your agent to high-speed inference infrastructure so you do not have to manage the hardware yourself.
Weights & Biases
Weights & Biases MCP lets your AI agent navigate your experiment history, monitor live training runs, and manage model artifacts without you having to leave your chat window. It turns your machine learning research into a conversation, giving you instant access to metrics, configs, and data lineage.
Google Sheets
Google Sheets MCP lets you read, write, and manage spreadsheet data through your AI agent. Stop wasting time on manual data entry or complex formulas. Just tell your agent to pull specific ranges, add new rows, or create entire new sheets on the fly. It handles the tedious work of keeping your data organized so you can focus on making decisions.