README.md

---
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/Qwen3-14B
pipeline_tag: feature-extraction
library_name: transformers
tags:
- sentence-transformers
---

# F2LLM-v2-14B-Preview

**F2LLM-v2-14B-Preview** is a multilingual embedding model trained from Qwen3-14B 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](https://www.sbert.net/) library:

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("codefuse-ai/F2LLM-v2-14B-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)
# (5120,) (4, 5120)

# Compute cosine similarity between the query and documents
similarity = model.similarity(query_embedding, document_embeddings)
print(similarity)
# tensor([[0.5889, 0.7934, 0.6786, 0.7778]])
```

### With Transformers

Or directly with the [Transformers](https://huggingface.co/docs/transformers/index) library:

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


model_path = "codefuse-ai/F2LLM-v2-14B-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, 5120]) torch.Size([4, 5120])

# Compute cosine similarity between the query and documents
similarity = query_embedding @ document_embeddings.T
print(similarity)
# tensor([[0.5898, 0.7930, 0.6797, 0.7773]], device='cuda:0',
#        dtype=torch.bfloat16, grad_fn=<MmBackward0>)
```

## Intermediate Checkpoints

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

## 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!