Title: EmbeddingGemma 300M %E2%80%94 embeddinggemma-300m
Canonical: https://llmeu.com/sl/models/embeddinggemma-300m/
Markdown mirror: https://llmeu.com/sl/models/embeddinggemma-300m.md
Machine-readable: https://llmeu.com/llms.txt (index) - https://llmeu.com/llms-full.txt (full catalogue) - https://llmeu.com/llms-catalogue.json
Provider: Google DeepMind
Licence: gemma
Hosting: catalogue only %E2%80%94 not hosted on LLM EU yet
Sources: https://huggingface.co/google/embeddinggemma-300m https://arxiv.org/abs/2509.20354

# EmbeddingGemma 300M

[Google DeepMind](/sl/providers/google/)

Nazadnje pregledano: 14. okt. 2026

## Suverenost in gostovanje

Ta model je v katalogu za referenco. Na LLM EU %C5%A1e ni gostovan in noben lastni endpoint ga tukaj ne stre%C5%BEe. Zahteve zanj gredo neposredno k ponudniku, pod lastnimi pogoji tega ponudnika.

## Povzetek

EmbeddingGemma je model za vdelavo besedila s 308M parametri in 768-dimenzionalnim izhodom, ki ga je mogo%C4%8De skraj%C5%A1ati na 512, 256 ali 128 dimenzij z u%C4%8Denjem Matryoshka reprezentacije. Kartica modela navaja najve%C4%8Djo dol%C5%BEino vnosa 2048 %C5%BEetonov in podatke za usposabljanje v ve%C4%8D kot 100 govorjenih jezikih. Izdan je pod Googlovimi pogoji Gemma in ne pod licenco OSI.

## Specifikacije

- Ponudnik
  Google DeepMind (US)
- Kontekstno okno
  2.0k
- Parametri
  0.3B
- Modalnosti
  embedding
- Licenca
  gemma %C2%B7 [licence](https://ai.google.dev/gemma/terms)
- Odprte ute%C5%BEi
  yes
- Datum izdaje
  17. jul. 2025
- Naloge
  [Pridobivanje (RAG)](/sl/tasks/rag/), [Ve%C4%8Djezi%C4%8Dna podpora](/sl/tasks/multilingual-support/), [Ob%C4%8Dutljivi podatki](/sl/tasks/private/), [Nizki stro%C5%A1ki](/sl/tasks/cheap/)

## Jeziki

**Mo%C4%8Dno:** en, de, fr, es, it, pt, nl, pl

**Zadostno:** sv, da, fi, cs, ro, el, uk, ru, ar, hi, zh, ja, ko, tr

#### Mo%C4%8Dno

- en
- de
- fr
- es
- it
- pt
- nl
- pl

#### Zadostno

- sv
- da
- fi
- cs
- ro
- el
- uk
- ru
- ar
- hi
- zh
- ja
- ko
- tr

Ni preverjeno: ponudnik te trditve ni dokumentiral. %E2%80%94 a language is listed as strong only where a source claims it; the card links the evidence where one exists.

## Kdaj ga uporabiti

#### Prednosti

- flexible output dimensions for cheaper vector indexes
- small enough to embed on CPU
- training data in over 100 languages

#### Omejitve

- 2048-token input limit
- Gemma terms of use, not an OSI-approved licence
- no retrieval benchmark has been run by LLM EU

## Pogosto zastavljena vpra%C5%A1anja

### Is EmbeddingGemma 300M served on LLM EU?

It has no endpoint in the catalogue, so it is catalogue only %E2%80%94 not hosted on LLM EU yet, with no region and no retention to report. A card whose status is "listed" is a catalogue entry, not a running deployment.

### Under what licence is EmbeddingGemma 300M released?

EmbeddingGemma 300M is recorded under gemma. The recorded licence text is https://ai.google.dev/gemma/terms. The weights are downloadable (open_weights is true). LLM EU republishes the licence id and the link and gives no legal advice; the licence text governs.

### How large is EmbeddingGemma 300M, and what context window does it have?

EmbeddingGemma 300M is recorded at 308M (safetensors total 302,863,104) (source: https://huggingface.co/api/models/google/embeddinggemma-300m), with a context window of 2048 tokens (source: https://huggingface.co/google/embeddinggemma-300m).

### Cene

Ta model je v katalogu za referenco. Na LLM EU %C5%A1e ni gostovan in noben lastni endpoint ga tukaj ne stre%C5%BEe. Zahteve zanj gredo neposredno k ponudniku, pod lastnimi pogoji tega ponudnika.

### Status preverjanja

Kadar polje nima vira, kartica to prika%C5%BEe kot nepreverjeno, namesto da bi ga izpolnila. Prazno polje je informacija.

LLM EU Verified pomeni dokumentirano, ne pravno certificirano. To ni ocena skladnosti z aktom o umetni inteligenci in LLM EU ni organ EU.

### Razli%C4%8Dice in variante

- catalogue %C2%B7 Samo v katalogu %C2%B7 [weights](https://huggingface.co/google/embeddinggemma-300m)

### Primer zahteve

```
curl https://api.llmeu.com/v1/chat/completions \
  -H "Authorization: Bearer $LLMEU_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"embeddinggemma-300m","messages":[{"role":"user","content":"Hello"}]}'
```

Za ta model ni name%C5%A1%C4%8Denega peskovnika: nobena kon%C4%8Dna to%C4%8Dka ga ne stre%C5%BEe, zato zahteva ne bi imela kam iti.

### Benchmarki

Ni objavljene ocene. To je vrzel, ne skrit rezultat.

### Viri

- [huggingface.co](https://huggingface.co/google/embeddinggemma-300m)
- [arxiv.org](https://arxiv.org/abs/2509.20354)

### Dnevnik sprememb kartice

- 17. jul. 2025 %E2%80%94 Datum izdaje
- 14. okt. 2026 %E2%80%94 Nazadnje pregledano

Nazadnje pregledano: 14. okt. 2026

### Naloge

- [Pridobivanje (RAG)](/sl/tasks/rag/)