Feature Extraction
sentence-transformers
Safetensors
English
embedding
retrieval
electrical-engineering
unsloth
information-retrieval
rag
semantic-search
arxiv:2509.20354
lora
Instructions to use disham993/electrical-embeddinggemma-ir_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use disham993/electrical-embeddinggemma-ir_lora with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("disham993/electrical-embeddinggemma-ir_lora") 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
- Unsloth Studio new
How to use disham993/electrical-embeddinggemma-ir_lora with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for disham993/electrical-embeddinggemma-ir_lora to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for disham993/electrical-embeddinggemma-ir_lora to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for disham993/electrical-embeddinggemma-ir_lora to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="disham993/electrical-embeddinggemma-ir_lora", max_seq_length=2048, )
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