SFR-Embedding-2_R
7B instruction-tuned text embedding model for research.
About
SFR-Embedding-2_R is a 7.11B-parameter embedding model from Salesforce Research built with a multi-stage training recipe, reaching 70+ on the MTEB benchmark at release. It supports instruction-based embedding with task-specific prompts and a 4,096-token context window, aimed at retrieval, classification, clustering, and semantic similarity work.
The catch: it is released for research purposes only under CC-BY-NC-4.0, so it is not an option for commercial products.
Specs
Context window
Pricing
Free quota
API access
Weights
Released
Benchmarks
70+ average on MTEB at release (Salesforce-reported).
Provider-reported unless a source says otherwise. Benchmarks are marketing-adjacent by nature — treat them as directional.
Use cases
Trust score — 7/10
Scored 7/10 (verified tier, metrics-v1). Evidence: Salesforce Research release; widely referenced in embedding research and RAG stacks; 2024 release, still cited in 2026 benchmarks; non-commercial license limits production use. License: CC-BY-NC-4.0, research purposes only per Hugging Face model card..
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 7.0/10
- Maintenance · recency, releases, issue load
- 5.0/10
- Openness · license + docs
- 4.0/10
- Manual review · sanity read + red-flag check
- 7.0/10
- · Salesforce Research release; widely referenced in embedding research and RAG stacks
- · 2024 release, still cited in 2026 benchmarks
- · non-commercial license limits production use