We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval.
GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and 64.38% on MTEB(Law, v1), demonstrating competitive performance among models under 1B parameters.
Our approach combines a two-stage training pipeline that first distills knowledge from a larger teacher model into a compact student architecture, then applies domain-specific fine-tuning with hard negative mining;
a carefully curated dataset of 3.4 million query-passage pairs, including 150,000 human-curated samples across diverse legal jurisdictions;
and an efficient inference architecture supporting multiple quantization levels (BF16, INT8, binary) enabling deployment in resource-constrained environments.
We provide detailed analysis of our training methodology, architectural choices, and comprehensive evaluation across legal retrieval tasks.
Our results demonstrate that domain-specific training with high-quality data can improve performance for specialized domain applications.