---
title: MiniCPM-Embedding-Light
canonical_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-Embedding-Light"
md_url: "https://www.modelscope.cn/models/OpenBMB/MiniCPM-Embedding-Light.md"
repository: OpenBMB/MiniCPM-Embedding-Light
last_updated: 2026-01-13
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
model_type:
  - minicpm
architectures:
  - MiniCPMModel
parameters: 433.9M
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - sentence-transformers
  - safetensors
frameworks:
  - Pytorch
downloads: 2032
stars: 7
tags:
  - mteb
  - sentence-transformers
---

# MiniCPM-Embedding-Light

> MiniCPM-Embedding-Light - OpenBMB 在 ModelScope 开源的模型。MiniCPM-Embedding-Light

- **Repository**: OpenBMB/MiniCPM-Embedding-Light
- **Tasks**: feature-extraction
- **Parameters**: 433.9M
- **Tags**: mteb, sentence-transformers
- **Downloads**: 2032
- **Stars**: 7
- **Last updated**: 2026-01-13

Source: https://www.modelscope.cn/models/OpenBMB/MiniCPM-Embedding-Light

---

## MiniCPM-Embedding-Light

**MiniCPM-Embedding-Light** 是面壁智能与清华大学自然语言处理实验室（THUNLP）、东北大学信息检索小组（NEUIR）共同开发的中英双语言文本嵌入模型，有如下特点：
- 出色的中文、英文检索能力。
- 出色的中英跨语言检索能力。
- 支持长文本（最长8192token）。
- 提供稠密向量与token级别的稀疏向量。
- 可变的稠密向量维度（套娃表征）。

MiniCPM-Embedding-Light结构上采取双向注意力和 Weighted Mean Pooling [1]。采取多阶段训练方式，共使用包括开源数据、机造数据、闭源数据在内的约 260M 条训练数据。

欢迎关注 UltraRAG 系列：

- 检索模型：[MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- 重排模型：[MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- 领域自适应RAG框架：[UltraRAG](https://github.com/openbmb/UltraRAG)

**MiniCPM-Embedding-Light** is a bilingual & cross-lingual text embedding model developed by ModelBest Inc. , THUNLP and NEUIR , featuring:

- Exceptional Chinese and English retrieval capabilities.
- Outstanding cross-lingual retrieval capabilities between Chinese and English.
- Long-text support (up to 8192 tokens).
- Dense vectors and token-level sparse vectors.
- Variable dense vector dimensions (Matryoshka representation [2]).

MiniCPM-Embedding-Light incorporates bidirectional attention and Weighted Mean Pooling [1] in its architecture. The model underwent multi-stage training using approximately 260 million training examples, including open-source, synthetic, and proprietary data.

We also invite you to explore the UltraRAG series:

- Retrieval Model: [MiniCPM-Embedding-Light](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
- Re-ranking Model: [MiniCPM-Reranker-Light](https://huggingface.co/openbmb/MiniCPM-Reranker-Light)
- Domain Adaptive RAG Framework: [UltraRAG](https://github.com/openbmb/UltraRAG)

[1] Muennighoff, N. (2022). Sgpt: Gpt sentence embeddings for semantic search. arXiv preprint arXiv:2202.08904.
[2] Kusupati, Aditya, et al. "Matryoshka representation learning." Advances in Neural Information Processing Systems 35 (2022): 30233-30249.

## 模型信息 Model Information

- 模型大小：440M
- 嵌入维度：1024
- 最大输入token数：8192

- Model Size: 440M
- Embedding Dimension: 1024
- Max Input Tokens: 8192

## 使用方法 Usage

### 输入格式 Input Format

本模型支持 query 侧指令，格式如下：

MiniCPM-Embedding-Light supports query-side instructions in the following format:

```
Instruction: {{ instruction }} Query: {{ query }}
```

例如：

For example:

```
Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么？
```

```
Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: However the warming trend is slower than most climate models have forecast.
```

也可以不提供指令，即采取如下格式：

MiniCPM-Embedding-Light also works in instruction-free mode in the following format:

```
Query: {{ query }}
```

### 环境要求 Requirements

```
transformers==4.37.2
```

### 示例脚本 Demo

#### Huggingface Transformers
```python
from transformers import AutoModel
import torch

model_name = "openbmb/MiniCPM-Embedding-Light"
model = AutoModel.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.float16).to("cuda")

# you can use flash_attention_2 for faster inference
# model = AutoModel.from_pretrained(model_name, trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda") 

model.eval()

queries = ["MiniCPM-o 2.6 A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone"]
passages = ["MiniCPM-o 2.6 is the latest and most capable model in the MiniCPM-o series. The model is built in an end-to-end fashion based on SigLip-400M, Whisper-medium-300M, ChatTTS-200M, and Qwen2.5-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.6, and introduces new features for real-time speech conversation and multimodal live streaming."]

embeddings_query_dense, embeddings_query_sparse = model.encode_query(queries, return_sparse_vectors=True)
embeddings_doc_dense, embeddings_doc_sparse = model.encode_corpus(passages, return_sparse_vectors=True)

dense_scores = (embeddings_query_dense @ embeddings_doc_dense.T)
print(dense_scores.tolist())  # [[0.6512398719787598]]
print(model.compute_sparse_score_dicts(embeddings_query_sparse,  embeddings_doc_sparse)) # [[0.27202296]]

dense_scores, sparse_scores, mixed_scores = model.compute_score(queries, passages)
print(dense_scores) # [[0.65123993]]
print(sparse_scores) # [[0.27202296]]
print(mixed_scores) # [[0.73284686]]
```

#### Sentence Transformers

```python
import torch
from sentence_transformers import SentenceTransformer


model_name = "openbmb/MiniCPM-Embedding-Light"
model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"torch_dtype": torch.float16})

# you can use flash_attention_2 for faster inference
# model = SentenceTransformer(model_name, trust_remote_code=True, model_kwargs={"attn_implementation": "flash_attention_2", "torch_dtype": torch.float16})

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query: "

embeddings_query = model.encode(queries, prompt=INSTRUCTION)
embeddings_doc = model.encode(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]
```

#### Infinity

```python
import asyncio
from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine
import numpy as np

array = AsyncEngineArray.from_args([
  EngineArgs(model_name_or_path = "openbmb/MiniCPM-Embedding-Light", engine="torch", dtype="float16", bettertransformer=False, pooling_method="mean", trust_remote_code=True),
])
queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"

INSTRUCTION = "Query:"
queries = [f"{INSTRUCTION} {query}" for query in queries]


async def embed_text(engine: AsyncEmbeddingEngine,sentences): 
    async with engine: 
        embeddings, usage = await engine.embed(sentences=sentences)
    return embeddings

queries_embedding = asyncio.run(embed_text(array[0],queries))
passages_embedding = asyncio.run(embed_text(array[0],passages))

scores = (np.array(queries_embedding) @ np.array(passages_embedding).T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183443665504456]]
```

#### FlagEmbedding
```python
from FlagEmbedding import FlagModel


model = FlagModel("openbmb/MiniCPM-Embedding-Light", 
                          query_instruction_for_retrieval="Query: ",
                          pooling_method="mean",
                          trust_remote_code=True,
                          normalize_embeddings=True,
                          use_fp16=True)
# You can hack the __init__() method of the FlagEmbedding BaseEmbedder class to use flash_attention_2 for faster inference
#  self.model = AutoModel.from_pretrained(
#             model_name_or_path,
#             trust_remote_code=trust_remote_code,
#             cache_dir=cache_dir,
#             # torch_dtype=torch.float16, # we need to add this line to use fp16
#             # attn_implementation="flash_attention_2", # we need to add this line to use flash_attention_2
#         )

queries = ["中国的首都是哪里？"] # "What is the capital of China?"
passages = ["beijing", "shanghai"] # "北京", "上海"


embeddings_query = model.encode_queries(queries)
embeddings_doc = model.encode_corpus(passages)

scores = (embeddings_query @ embeddings_doc.T)
print(scores.tolist())  # [[0.40356746315956116, 0.36183440685272217]]
```


## 实验结果 Evaluation Results

### 中文与英文检索结果 CN/EN Retrieval Results

| 模型 Model                                         | C-MTEB/Retrieval(NDCG@10) | BEIR(NDCG@10) |
|----------------------------------------------------|-------------------|---------------|
| bge-large-zh-v1.5                                  | 70.46             | -             |
| gte-large-zh                                       | 72.49             | -             |
| Conan-embedding-v1                                 | 76.67             |               |
| bge-large-en-v1.5                                  | -                 | 54.29         |
| modernbert-embed-large                             | -                 | 54.36         |
| snowflake-arctic-embed-l                           | -                 | 55.98         |
| gte-en-large-v1.5                                  | -                 | 57.91         |
| me5-large                                          | 63.66             | 51.43         |
| bge-m3(Dense)                                      | 65.43             | 48.82         |
| gte-multilingual-base(Dense)                       | 71.95             | 51.08         |
| jina-embeddings-v3                                 | 68.60              | 53.88         |
| gte-Qwen2-1.5B-instruct                            | 71.86             | 58.29         |
| MiniCPM-Embedding                                  | 76.76             | 58.56         |
| MiniCPM-Embedding-Light(Dense)                          | 72.71             | 55.27         |
| MiniCPM-Embedding-Light(Dense+Sparse)                   | 73.13             | 56.31         |
| MiniCPM-Embedding-Light(Dense+Sparse)+MiniCPM-Reranker-Light | 76.34             | 61.49         |


### 中英跨语言检索结果 CN-EN Cross-lingual Retrieval Results

| 模型  Model                | MKQA En-Zh_CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
|------------------------------|--------------------|--------------------|--------------------|
| me5-large                                   | 44.3               | 9.01               | 25.33              |
| bge-m3(Dense)                               | 66.4               | 30.49              | 41.09              |
| gte-multilingual-base(Dense)                | 68.2               | 39.46              | 45.86              |
| MiniCPM-Embedding                           | 72.95              | 52.65              | 49.95              |
| MiniCPM-Embedding-Light(Dense)                   | 68.29              | 41.17              | 45.83              |
| MiniCPM-Embedding-Light(Dense)+MiniCPM-Reranker-Light | 71.86              | 54.32              | 56.50               |

## 许可证 License

- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/openbmb/MiniCPM/blob/main/LICENSE)开源。
- MiniCPM-Embedding-Light 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
- MiniCPM-Embedding-Light 模型权重对学术研究完全开放。如需将模型用于商业用途，请填写[此问卷](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)。

* The code in this repo is released under the [Apache-2.0](https://github.com/openbmb/MiniCPM/blob/main/LICENSE) License. 
* The usage of MiniCPM-Embedding-Light model weights must strictly follow [MiniCPM Model License.md](https://github.com/openbmb/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM-Embedding-Light are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-Embedding-Light weights are also available for free commercial use.