EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Lobsters·Aug 29·5 min read
InferenceFS: Never worry about data again! (Again!)
TL;DR
InferenceFS revolutionizes data storage by using large language models to infer data contents from minimal metadata, drastically reducing storage needs. Instead of storing data, it only requires filenames, allowing for significant space savings.
✦ Why It Matters
Consider adopting InferenceFS to drastically reduce your data storage costs and simplify management.
Key Takeaways
- Install InferenceFS via PyPI for immediate use.
- Utilize the Gemini backend for optimal performance and support.
- Store large datasets using minimal metadata to save costs.
- Leverage built-in caching for efficient repeated file access.
How It Works
InferenceFS operates by querying a large language model to infer the contents of files based solely on their filenames. When a file is accessed, the system sends a request to the LLM, which generates the file's contents based on its training data.
This approach allows for significant storage savings, as the model's weights effectively hold the data, while only the filenames are stored as metadata.