KaLM-Embedding-Gemma3-12B-2511 / README.md

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metadata

datasets:
  - KaLM-Embedding/KaLM-embedding-finetuning-data
base_model:
  - google/gemma-3-12b-pt
pipeline_tag: feature-extraction
library_name: sentence-transformers
tags:
  - Retrieval
  - STS
  - Classification
  - Clustering
  - Reranking
  - vllm
license: other
license_name: tencent-kalm-embedding-community
extra_gated_eu_disallowed: true

KaLM-Embedding-Gemma3-12B-2511

Short Description

KaLM-Embedding-Gemma3-12B-2511 is a versatile and compact embedding model, which achieves SOTA performance in MMTEB (due to 11-2025).

MMTEB Evaluation Results

Rank (Borda)	Model	Mean (Task)	Mean (TaskType)	Bitext Mining	Classification	Clustering	Instruction Reranking	Multilabel Classification	Pair Classification	Reranking	Retrieval	STS
1	KaLM-Embedding-Gemma3-12B-2511	72.32	62.51	83.76	77.88	55.77	5.49	33.03	84.73	67.27	75.66	79.02
2	llama-embed-nemotron-8b	69.46	61.09	81.72	73.21	54.35	10.82	29.86	83.97	67.78	68.69	79.41
3	Qwen3-Embedding-8B	70.58	61.69	80.89	74.00	57.65	10.06	28.66	86.40	65.63	70.88	81.08
4	gemini-embedding-001	68.37	59.59	79.28	71.82	54.59	5.18	29.16	83.63	65.58	67.71	79.40
5	Qwen3-Embedding-4B	69.45	60.86	79.36	72.33	57.15	11.56	26.77	85.05	65.08	69.60	80.86
6	Qwen3-Embedding-0.6B	64.34	56.01	72.23	66.83	52.33	5.09	24.59	80.83	61.41	64.65	76.17
7	gte-Qwen2-7B-instruct	62.51	55.93	73.92	61.55	52.77	4.94	25.48	85.13	65.55	60.08	73.98
8	Linq-Embed-Mistral	61.47	54.14	70.34	62.24	50.60	0.94	24.77	80.43	64.37	58.69	74.86
9	multilingual-e5-large-instruct	63.22	55.08	80.13	64.94	50.75	-0.40	22.91	80.86	62.61	57.12	76.81
10	embeddinggemma-300m	61.15	54.31	64.40	60.90	51.17	5.61	24.82	81.40	63.25	62.49	74.73

Model Details

Model Size: 11.76B
Embedding Dimension: 3840
Max Input Tokens: 32k
MRL dimensions: 3840, 2048, 1024, 512, 256, 128, and 64
Pooling: lasttoken pooling

Training Recipe

High-quality supervised finetuning

📑 Open-source Plan

Model Checkpoint
Training and Evaluation Code: HITsz-TMG/KaLM-Embedding
Technical Report: KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model
Pre-training Data: Pre-training Data
Fine-tuning Data: Fine-tuning Data

Usage

sentence-transformers support

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

You can use the model like this:

from sentence_transformers import SentenceTransformer
import torch

model = SentenceTransformer(
    "tencent/KaLM-Embedding-Gemma3-12B-2511",
    trust_remote_code=True,
    model_kwargs={
        "torch_dtype": torch.bfloat16,
        "attn_implementation": "flash_attention_2",  # Optional
    },
)
model.max_seq_length = 512

sentences = ["This is an example sentence", "Each sentence is converted"]
prompt = "Instruct: Classifying the category of french news.\nQuery:"
embeddings = model.encode(
    sentences,
    prompt=prompt,
    normalize_embeddings=True,
    batch_size=256,
    show_progress_bar=True,
)
print(embeddings)
'''
[[-0.01867676  0.02319336  0.00280762 ... -0.02075195  0.00196838
  -0.0703125 ]
 [-0.0067749   0.03491211  0.01434326 ... -0.0043335   0.00509644
  -0.04174805]]
'''

Or you can use encode_query and encode_document to automatically add the default prompt for queries ("Instruct: Given a query, retrieve documents that answer the query \n Query: ") and documents (""), respectively.

from sentence_transformers import SentenceTransformer
import torch

model = SentenceTransformer(
    "tencent/KaLM-Embedding-Gemma3-12B-2511",
    trust_remote_code=True,
    model_kwargs={
        "torch_dtype": torch.bfloat16,
        "attn_implementation": "flash_attention_2",  # Optional
    },
)
model.max_seq_length = 512

queries = [
    "What is the capital of China?",
    "Explain gravity",
]
documents = [
    "The capital of China is Beijing.",
    "Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun.",
]

query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)

similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
'''
tensor([[0.9034, 0.2563],
        [0.3153, 0.7396]])
'''

Citation

If you find this model useful, please consider giving a star and citation.

@misc{zhao2025kalmembeddingv2,
      title={KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model}, 
      author={Xinping Zhao and Xinshuo Hu and Zifei Shan and Shouzheng Huang and Yao Zhou and Xin Zhang and Zetian Sun and Zhenyu Liu and Dongfang Li and Xinyuan Wei and Youcheng Pan and Yang Xiang and Meishan Zhang and Haofen Wang and Jun Yu and Baotian Hu and Min Zhang},
      year={2025},
      eprint={2506.20923},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.20923}, 
}

@misc{hu2025kalmembedding,
      title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model}, 
      author={Xinshuo Hu and Zifei Shan and Xinping Zhao and Zetian Sun and Zhenyu Liu and Dongfang Li and Shaolin Ye and Xinyuan Wei and Qian Chen and Baotian Hu and Haofen Wang and Jun Yu and Min Zhang},
      year={2025},
      eprint={2501.01028},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.01028}, 
}

Contact

If you encounter any issue, feel free to contact us via the email: [email protected], [email protected]