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] - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 672d9eccd9e0839593293adc88816c63dbd0db4d8c0eac7a5b646de775ab45b8
- Size of remote file:
- 318 MB
- SHA256:
- b8418d33aec981b542f7f71ac7737be247b1159a50748304f52ce92ea84b3fd1
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