Feature Extraction
sentence-transformers
Safetensors
Transformers
English
qwen3
text-embeddings-inference
Instructions to use codefuse-ai/F2LLM-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codefuse-ai/F2LLM-4B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-4B") 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] - Transformers
How to use codefuse-ai/F2LLM-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-4B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-4B") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c2268ef2dbd8cb158ceef8efa326a348c3b1645c00ecfae555870e6f48ce8c2
- Size of remote file:
- 11.4 MB
- SHA256:
- 38360d5a512a43641b36d6fba2df87b8a3f5464c6b5c76f03e82d6d795175566
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