F2LLM-v2-1.7B-Preview

F2LLM-v2-1.7B-Preview

F2LLM-v2-1.7B-Preview is a multilingual embedding model trained from Qwen3-1.7B on a corpus of 27 million samples, spanning over 100 natural and programming languages. It is a "preview" version trained without instructions and intended to serve as a foundation for downstream embedding tasks and further fine-tuning.

F2LLM-v2 is fully open. We release base models in 5 sizes, instruct models in 8 sizes, the training data, the training code, and intermediate checkpoints. The three smallest instruct models are pruned and trained from the 0.6B base model.

Usage

With Sentence Transformers

To encode text with the Sentence Transformers library:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("codefuse-ai/F2LLM-v2-1.7B-Preview", device="cuda:0", model_kwargs={"torch_dtype": "bfloat16"})

# Some sample query and documents
query = "What is F2LLM used for?"
documents = [
    'We present F2LLM, a family of fully open embedding LLMs that achieve a strong balance between model size, training data, and embedding performance.',
    'F2LLM is a model for computing text embeddings that can be used for various NLP tasks such as information retrieval, semantic search, and text classification.',
    'F2LLM 是 CodeFuse 开源的系列嵌入模型。',
    'F2LLM — это модель вычисления встраивания текста, которую можно использовать для различных задач НЛП, таких как поиск информации, семантический поиск и классификация текста.'
]

# Encode the query and documents
query_embedding = model.encode(query)
document_embeddings = model.encode(documents)
print(query_embedding.shape, document_embeddings.shape)
# (2048,) (4, 2048)

# Compute cosine similarity between the query and documents
similarity = model.similarity(query_embedding, document_embeddings)
print(similarity)
# tensor([[0.6016, 0.7691, 0.6831, 0.8017]])

With Transformers

Or directly with the Transformers library:

from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn.functional as F


model_path = "codefuse-ai/F2LLM-v2-1.7B-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map={'': 0})

query = "What is F2LLM used for?"

documents = [
    'We present F2LLM, a family of fully open embedding LLMs that achieve a strong balance between model size, training data, and embedding performance.',
    'F2LLM is a model for computing text embeddings that can be used for various NLP tasks such as information retrieval, semantic search, and text classification.',
    'F2LLM 是 CodeFuse 开源的系列嵌入模型。',
    'F2LLM — это модель вычисления встраивания текста, которую можно использовать для различных задач НЛП, таких как поиск информации, семантический поиск и классификация текста.'
]

def encode(sentences):
    batch_size = len(sentences)
    # the tokenizer will automatically add eos token
    tokenized_inputs = tokenizer(sentences, padding=True, return_tensors='pt').to(model.device)
    last_hidden_state = model(**tokenized_inputs).last_hidden_state
    eos_positions = tokenized_inputs.attention_mask.sum(dim=1) - 1
    embeddings = last_hidden_state[torch.arange(batch_size, device=model.device), eos_positions]
    embeddings = F.normalize(embeddings, p=2, dim=1)
    return embeddings

# Encode the query and documents
query_embedding = encode([query])
document_embeddings = encode(documents)
print(query_embedding.shape, document_embeddings.shape)
# torch.Size([1, 2048]) torch.Size([4, 2048])

# Compute cosine similarity between the query and documents
similarity = query_embedding @ document_embeddings.T
print(similarity)
# tensor([[0.6016, 0.7695, 0.6836, 0.8008]], device='cuda:0',
#        dtype=torch.bfloat16, grad_fn=<MmBackward0>)

Intermediate Checkpoints

To facilitate future research, we release intermediate checkpoints in the intermediate_checkpoints branch.

Citation

@misc{f2llm-v2,
      title={F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World}, 
      author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
      year={2026},
      eprint={2603.19223},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.19223}, 
}