Instructions to use RESMP-DEV/jina-code-embeddings-1.5b-mxfp4-block-gptq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use RESMP-DEV/jina-code-embeddings-1.5b-mxfp4-block-gptq with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir jina-code-embeddings-1.5b-mxfp4-block-gptq RESMP-DEV/jina-code-embeddings-1.5b-mxfp4-block-gptq
- sentence-transformers
How to use RESMP-DEV/jina-code-embeddings-1.5b-mxfp4-block-gptq with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("RESMP-DEV/jina-code-embeddings-1.5b-mxfp4-block-gptq") 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
- Local Apps Settings
- LM Studio
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| "scope": "exact 32x32 Hessian and GPTQ error propagation within each MXFP4 group", | |
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