KaLM-embedding-multilingual-mini-instruct-v1-GGUF

<h1 align="center">KaLM-Embedding</h1>

KaLM-Embedding is a series of embedding models adapted from auto-regressive LLMs with superior training data.

KaLM-embedding-multilingual-mini is trained from Qwen/Qwen2-0.5B with massive weakly-supervised pre-training and supervised fine-tuning data.

📑 Open-source Plan

• Model Checkpoint
  • KaLM-embedding-multilingual-mini-v1
  • KaLM-embedding-multilingual-mini-instruct-v1
  • KaLM-embedding-multilingual-mini-instruct-v1.5
  • KaLM-embedding-multilingual-max-v1
• Training and Evaluation Code: HITsz-TMG/KaLM-Embedding
• Technical Report: KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model
• Training Data

Evaluation

Requirements

Since we have used the Qwen2 model, we advise you to install transformers>=4.37.0, or you might encounter the following error:

KeyError: 'qwen2'

Usage

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

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer


sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME_OR_PATH}')   # Do NOT set trust_remote_code
model.max_seq_length = 512

embeddings = model.encode(
    sentences, 
    normalize_embeddings=True,
    batch_size=256, 
    show_progress_bar=True
    )
print(embeddings)

<!-- We add instruction for asymmetric tasks: retrieval, reranking, classification and clustering. -->

We add instruction for classification and clustering. If you want to add instruction to the query (no instruction for the corpus), you can use the model like this:

from sentence_transformers import SentenceTransformer


sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME_OR_PATH}')   # Do NOT set trust_remote_code
model.max_seq_length = 512

prompt = "Instruct: Classifying the category of french news. \n Query: "
embeddings = model.encode(
    sentences, 
    prompt=prompt,
    normalize_embeddings=True,
    batch_size=256, 
    show_progress_bar=True
    )
print(embeddings)

Citation

Please cite the repo if you use the model or code in this repo.

@article{hu2025kalm,
  title={KaLM-Embedding: Superior Training Data Brings A Stronger Embedding Model},
  author={Hu, Xinshuo and Shan, Zifei and Zhao, Xinping and Sun, Zetian and Liu, Zhenyu and Li, Dongfang and Ye, Shaolin and Wei, Xinyuan and Chen, Qian and Hu, Baotian and others},
  journal={arXiv preprint arXiv:2501.01028},
  year={2025}
}

Contact

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