Instructions to use codefuse-ai/F2LLM-v2-80M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/F2LLM-v2-80M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/F2LLM-v2-80M")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("codefuse-ai/F2LLM-v2-80M") model = AutoModel.from_pretrained("codefuse-ai/F2LLM-v2-80M") - sentence-transformers
How to use codefuse-ai/F2LLM-v2-80M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/F2LLM-v2-80M") 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
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README.md
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# Compute cosine similarity between the query and documents
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similarity = model.similarity(query_embedding, document_embeddings)
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print(similarity)
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# tensor([[0.
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```
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### With Transformers
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# Compute cosine similarity between the query and documents
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similarity = model.similarity(query_embedding, document_embeddings)
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print(similarity)
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# tensor([[0.6968, 0.7818, 0.7165, 0.8374]])
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```
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### With Transformers
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