F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data
This provides a more budget-friendly and reproducible baseline for embedding model research, though it is incremental as it builds on existing foundation models.
The paper tackles the problem of high training costs for state-of-the-art embedding models by introducing F2LLM, a suite of embedding models finetuned on 6 million open-source data points, achieving 2nd place among 4B-parameter models and 1st in the 1B-2B range on the MTEB English leaderboard.
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.