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# Build a Domain-Specific Embedding Model in Under a Day

**[Hugging Face](https://daily.dev/sources/huggingface)** · 13 min read · 0 upvotes · 0 comments

## Summary

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.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://huggingface.co/blog/nvidia/domain-specific-embedding-finetune>

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---

Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#rag](https://daily.dev/tags/rag), [#embeddings](https://daily.dev/tags/embeddings)

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