F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data

Ziyin Zhang, Liaozihan, Hang Yu, Peng Di, Rui Wang

F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data: 48 upvotes on Hugging Face Daily Papers, #6 of 52 papers on 2025-10-03. Day-by-day upvote history.

We introduce F2LLM - Foundation to Feature Large Language Models, a suite of state-of-the-art embedding models in three sizes: 0.6B, 1.7B, and 4B. Unlike previous top-ranking embedding models that require massive contrastive pretraining, sophisticated training pipelines, and costly synthetic training data, F2LLM is directly finetuned from foundation models on 6 million query-document-negative tuples curated from open-source, non-synthetic datasets, striking a strong balance between training cost, model size, and embedding performance. On the MTEB English leaderboard, F2LLM-4B ranks 2nd among models with approximately 4B parameters and 7th overall, while F2LLM-1.7B ranks 1st among models in the 1B-2B size range. To facilitate future research in the field, we release the models, training dataset, and code, positioning F2LLM as a strong, reproducible, and budget-friendly baseline for future works.

Paper page on Hugging Face · arXiv

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