multi-qa-mpnet-base-dot-v1
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and was designed for semantic search. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search
Params
110 M
Context
512
Downloads 30d
1.9 M
Likes
193
Download history
daily snapshots · 10 days2.0 M1.9 M
Jul 28Jul 31Aug 3Aug 6
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 0.4 GB | 1.0 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Specifications
- Architecture
- MPNetForMaskedLM
- Parameters
- 110 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 30,527
- Layers / heads
- 12 / 12
- First seen on the Hub
- 2022-03-02
- Training datasets
- flax-sentence-embeddings/stackexchange_xml, ms_marco, gooaq, yahoo_answers_topics, search_qa, eli5, natural_questions, trivia_qa, embedding-data/QQP, embedding-data/PAQ_pairs, embedding-data/Amazon-QA, embedding-data/WikiAnswers
- Added to our catalog
- 2026-07-28
Compare with
Sponsored · GPU cloud
Not enough VRAM?
Spin up a 24 GB L4 instance in 40 seconds. $0.44/hr.