llm-retriever-base
text-embeddingintfloat · intfloat · released
About
LLM-Retriever-Base is intfloat's compact (0.11B) open-weight dense retriever for embedding queries and passages into a shared space — designed for retrieving in-context examples and documents to feed LLMs.
Architecture
- Type
- encoder
- Parameters
- 109M
LLM-Retriever base: a BERT-base-scale (0.11B) dense retriever that embeds queries and passages into a shared vector space for in-context example / document retrieval. Open-weight, transformers library.
Memory
- Weights (BF16)
- 0.22 GB
- Activation estimate
- 0.10 GB
Pricing
Free — open weights
Self-host on your own GPU. The calculator surfaces GPU-hours cost on the hardware page instead of an API price.
Provenance
- Source
- huggingface.co
- License
- mit
- Last verified
- 2026-06-22
embeddingretrievaldense-retrieveropen-weight