Model reference · open weights
PwC-Embedding_expr is an open-weight embedding model from SamilPwC-AXNode-GenAI. PwC-Embedding_expr (FP32) weighs 1.1 GB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the SamilPwC-AXNode-GenAI/PwC-Embedding_expr model card, 2026-10-01
What it is
| Released by | SamilPwC-AXNode-GenAI |
|---|---|
| Released | 2025-08-12 |
| Parameters | 560M |
| VRAM | 1.1 GB for the weights |
What it runs on
| Card | Runs | Memory |
|---|---|---|
| RTX 3060 12 GB | fits | 11.6 GB |
| RTX 4060 Ti 16 GB | fits | 15.4 GB |
| RTX 3090 24 GB | fits | 23.4 GB |
| RTX 4090 24 GB | fits | 23.4 GB |
| RTX 5090 32 GB | fits | 31.0 GB |
| L40S 48 GB | fits | 44.0 GB |
| A100 80 GB | fits | 78.2 GB |
| H100 80 GB | fits | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | fits | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | fits | 107 GB |
| H200 141 GB | fits | 138 GB |
| B200 180 GB | fits | 176 GB |
From the model card
We trained the PwC-Embedding-expr model on top of the multilingual-e5-large-instruct embedding model. To enhance performance in Korean, we applied our curated augmentation to STS datasets and fine-tuned the E5 model using a carefully balanced ratio across datasets.
⚠️ This is an experimental model and is under continuous development.
PwC-Embedding_expr was evaluated on the Korean subset of MTEB. A leaderboard link will be added once it is published.
| Task | PwC-Embedding_expr |
|---|---|
| KLUE-STS | 0.88 |
| KLUE-TC | 0.73 |
| Ko-StrategyQA | 0.80 |
| KorSTS | 0.84 |
| MIRACL-Reranking | 0.72 |
| MIRACL-Retrieval | 0.65 |
| Average | 0.77 |
It works with the dependencies included in the latest version of MTEB.
TBD (technical report expected September 2025)
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.