Instructions to use codefuse-ai/F2LLM-v2-160M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/F2LLM-v2-160M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-v2-160M")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-v2-160M") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-v2-160M") - sentence-transformers
How to use codefuse-ai/F2LLM-v2-160M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-v2-160M") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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## Intermediate Checkpoints
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To facilitate future research, we release intermediate checkpoints in the `intermediate_checkpoints` branch.
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## Intermediate Checkpoints
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To facilitate future research, we release intermediate checkpoints in the `intermediate_checkpoints` branch.
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## Citation
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```
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@misc{f2llm-v2,
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title={F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World},
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author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
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year={2026},
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eprint={2603.19223},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2603.19223},
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}
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```
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