Feature Extraction
Transformers
Safetensors
sentence-transformers
Chinese
English
c2llm
code
custom_code
Instructions to use codefuse-ai/C2LLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codefuse-ai/C2LLM-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="codefuse-ai/C2LLM-7B", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codefuse-ai/C2LLM-7B", trust_remote_code=True, dtype="auto") - sentence-transformers
How to use codefuse-ai/C2LLM-7B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("codefuse-ai/C2LLM-7B", trust_remote_code=True) 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
- Xet hash:
- 4bd2f092eeec244c0448f13e31edd4b25e625568713e4155993a3dea90c7437a
- Size of remote file:
- 11.4 MB
- SHA256:
- 9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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