README.md

metadata

license: apache-2.0
language:
  - en
  - es
  - fr
  - de
  - ru
  - nl
  - vi
  - zh
  - hi
  - id
  - it
  - ja
  - pt
  - pl
  - ar
  - ko
  - uk
  - th
  - ca
  - cs
  - gl
  - tl
  - eu
  - hy
  - ne
  - fa
  - my
  - lo
  - km
  - az
  - tg
  - sv
  - si
  - da
  - tr
  - sw
  - fi
  - ro
  - 'no'
  - hu
  - he
  - el
  - sk
  - bg
base_model:
  - Qwen/Qwen3-8B
pipeline_tag: feature-extraction
library_name: transformers
tags:
  - sentence-transformers

F2LLM-v2-8B-Preview

F2LLM-v2-8B-Preview is a multilingual embedding model trained from Qwen3-8B 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.

Usage

With Sentence Transformers

To encode text with the Sentence Transformers library:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("codefuse-ai/F2LLM-v2-8B-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)
# (4096,) (4, 4096)

# Compute cosine similarity between the query and documents
similarity = model.similarity(query_embedding, document_embeddings)
print(similarity)
# tensor([[0.6329, 0.8003, 0.6361, 0.8267]])

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-v2-8B-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, 4096]) torch.Size([4, 4096])

# Compute cosine similarity between the query and documents
similarity = query_embedding @ document_embeddings.T
print(similarity)
# tensor([[0.6328, 0.8008, 0.6328, 0.8242]], device='cuda:0',
#        dtype=torch.bfloat16, grad_fn=<MmBackward0>)

Future Releases

We are committed to the open-source community and will soon release:

• The Finetuned Version: Optimized for downstream tasks, with state-of-the-art performance on MTEB.
• The Training Data: We will be releasing the data used to train F2LLM-v2 to help advance the field of multilingual embeddings.

Stay tuned for more updates!