Feature Extraction
sentence-transformers
Safetensors
Transformers
English
qwen3
text-embeddings-inference
Instructions to use codefuse-ai/F2LLM-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use codefuse-ai/F2LLM-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-0.6B") 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-0.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-0.6B")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-0.6B") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-0.6B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, library name, and paper link
#1
by nielsr HF Staff - opened
This PR enhances the model card for the F2LLM-0.6B model by:
- Adding the
pipeline_tag: feature-extractionto the YAML metadata, which improves the model's discoverability for embedding tasks on the Hugging Face Hub. - Adding the
library_name: transformersto the YAML metadata. This is justified by the existingUsagesection's code snippet, which utilizestransformers.AutoModelandAutoTokenizer, and will enable the automatic "How to use" widget on the model page. - Setting the main title of the model card to reflect the paper's title and adding a direct link to the paper, providing a clearer overview and easier access to the research.
Geralt-Targaryen changed pull request status to merged