all-MiniLM-L6-v2
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Params
20 M
Context
512
Downloads 30d
254.1 M
Likes
6,085
Download history
daily snapshots · 55 days
▲ 3.5 M in the last 30 days (1.4%)
257.8 M241.2 M
Aug 22Sep 1Sep 11Sep 20
259.7 M239.2 M
Jul 28Aug 15Sep 2Sep 20
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 | 0.1 GB | 0.6 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
- BertModel
- Parameters
- 20 M
- Tensor type
- F32
- Context length
- 512
- Vocabulary
- 30,522
- Layers / heads
- 6 / 12
- Licence
- apache-2.0
- First seen on the Hub
- 2022-03-02
- Training datasets
- s2orc, flax-sentence-embeddings/stackexchange_xml, ms_marco, gooaq, yahoo_answers_topics, code_search_net, search_qa, eli5, snli, multi_nli, wikihow, natural_questions, trivia_qa, embedding-data/sentence-compression, embedding-data/flickr30k-captions, embedding-data/altlex, embedding-data/simple-wiki, embedding-data/QQP, embedding-data/SPECTER, embedding-data/PAQ_pairs, embedding-data/WikiAnswers
- Added to our catalog
- 2026-07-28
Family
Base model and the most-downloaded derivatives in the catalog.
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