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Hugging Face

A step-by-step guide to fine-tuning a domain-specific embedding model on a single GPU in under a day, with no manual labeling required. The pipeline uses NVIDIA's NeMo toolchain to: (1) generate synthetic QA training pairs from raw documents using an LLM, (2) mine hard negatives for contrastive training, (3) fine-tune a 1B-parameter bi-encoder model (Llama-Nemotron-Embed-1B-v2), (4) evaluate with BEIR metrics, and (5) export to ONNX/TensorRT and deploy via NVIDIA NIM. Results show 10%+ gains in NDCG@10 and Recall@10 on NVIDIA docs, and Atlassian achieved a 26.7% Recall@60 improvement on their Jira dataset. The full pipeline runs in 6 CLI commands and completes in 2–3 hours for small corpora.

Build a Domain-Specific Embedding Model in Under a Day

📚 Step 1: Generate Training Data from Documents

⛏️ Step 2: Mine Hard Negatives (and Why They Matter)

🔍 Step 3: Understand Multi-Hop Questions and Why They Improve Retrieval

🧠 Step 4: Fine-Tune the Embedding Model