With a single GPU and less than a day of training time, you can transform a general-purpose embedding model into one that truly understands your domain, no manual labeling required. To help you hit the ground running, we are also releasing a ready-to-use synthetic training dataset generated from NVIDIA's public documentation using this exact pipeline. Using this data and the recipe, we saw over 10% improvement in both Recall@10 and NDCG@10. Atlassian applied this recipe to fine-tune on their JIRA dataset, increasing Recall@60 from 0.751 to 0.951, a 26% improvement - on a single GPU.

By the end of this post, you’ll know how to:
📄 Generate training data from domain documents without labeled data
🎯 Use hard negative mining for effective contrastive training
🔗 Improve embedding quality with multi-hop queries
⚙️ Fine-tune a bi-encoder embedding model
📊 Evaluate whether fine-tuning improves retrieval
🚀 Deploy the fine-tuned model in your pipeline

In this tutorial, we will finetune the base model Llama-Nemotron-Embed-1B-v2 - a 1-billion-parameter embedding model that balances quality and inference cost. To get started, follow this setup guide.

Fine-tuning an embedding model requires thousands of (query, relevant document) pairs. Most use cases don’t have this data readily available. Creating it manually is expensive, slow, and often biased by the annotator’s personal interpretation of what’s “relevant.”
Instead of labeling data by hand, you can use an LLM (nvidia/nemotron-3-nano-30b-a3b) to read your documents and automatically generate high-quality synthetic question–answer pairs.

Behind the scenes, this runs a four-stage synthetic data generation (SDG) pipeline powered by NeMo Data Designer :

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