Model reference · open weights
bge-large-zh is an open-weight embedding model from BAAI. bge-large-zh (FP32) weighs 651 MB; the smallest configuration that runs it is RTX 3060 12 GB.
What it is
| Released by | BAAI |
|---|---|
| Released | 2023-09-12 |
| VRAM | 651 MB 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 |
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| bge-large-zh-v1.5 ↗ | — | BF16 | 1.3 GB | RTX 3060 12 GB |
| bge-large-zh (above) ↗ | 326M | FP32 | 651 MB | RTX 3060 12 GB |
From the model card
For more details please refer to our Github: FlagEmbedding.
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3.
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical Report and Code. :fire:
1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report :fire:
12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report :fire:
11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire:
10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report
09/15/2023: The technical report and massive training data of BGE has been released
09/12/2023: New models:
BAAI/bge-reranker-base and BAAI/bge-reranker-large, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.bge-*-v1.5 embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning.
08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.
08/05/2023: Release base-scale and small-scale models, best performance among the models of the same size 🤗
08/02/2023: Release bge-large-*(short for BAAI General Embedding) Models, rank 1st on MTEB and C-MTEB benchmark! :tada: :tada:
08/01/2023: We release the Chinese Massive Text Embedding Benchmark (C-MTEB), consisting of 31 test dataset.
bge is short for BAAI general embedding.
| Model | Language | Description | query instruction for retrieval [1] | |
|---|---|---|---|---|
| BAAI/bge-m3 | Multilingual | Inference Fine-tune | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| BAAI/llm-embedder | English | Inference Fine-tune | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See README |
| BAAI/bge-reranker-large | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | |
| BAAI/bge-reranker-base | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.