🔢 Embedding Models
Collection
Vector embedding models for on-device RAG, semantic search, and retrieval-augmented generation. Compact enough to run locally on mobile hardware. • 4 items • Updated
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)EmbeddingGemma-300M-mobile
🔢 Embedding — This is not a chat model.
Embedding model — use for vector embeddings, not chat. Use sentence-transformers or similar.
Model Details
| Attribute | Value |
|---|---|
| Base Model | google/embeddinggemma-300m |
| Type | EMBEDDING |
| License | gemma |
Usage
from sentence_transformers import SentenceTransformer; model = SentenceTransformer('dispatchAI/EmbeddingGemma-300M-mobile')
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dispatchAI/EmbeddingGemma-300M-mobile", filename="model.gguf", )