PrêtGPU TEE

Sentence Transformers: all-MiniLM-L6-v2

ID du modèlesentence-transformers/all-minilm-l6-v2

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.

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entrée

$0.0050/M

sortie

Free/M

contexte

512

créé

25 nov. 2025

Forme d’API prise en charge

entrée

text · embeddings

sortie

embeddings

outils

Non सूचीé

mode JSON

Non सूचीé

Vérification

signature

ID de réponse

attestation

GPU TEE

fournisseur

Phala

Provider

Phala

GPU TEE

entrée

$0.0050/M

sortie

Free/M

contexte

512

$ pip install openai

1from openai import OpenAI
2import os
3
4MODEL = "sentence-transformers/all-minilm-l6-v2"
5
6client = OpenAI(
7    base_url="https://api.redpill.ai/v1",
8    api_key=os.environ["REDPILL_API_KEY"],
9)
10
11response = client.embeddings.create(
12    model=MODEL,
13    input="RedPill protects model calls with hardware TEE",
14)
15
16print(response.data[0].embedding[:8])

$ npm install openai

1import OpenAI from "openai"
2
3const MODEL = "sentence-transformers/all-minilm-l6-v2"
4
5const openai = new OpenAI({
6  baseURL: "https://api.redpill.ai/v1",
7  apiKey: process.env.REDPILL_API_KEY,
8})
9
10const response = await openai.embeddings.create({
11  model: MODEL,
12  input: "RedPill protects model calls with hardware TEE",
13})
14
15console.log(response.data[0].embedding.slice(0, 8))

1curl https://api.redpill.ai/v1/embeddings \
2  -H "Authorization: Bearer $REDPILL_API_KEY" \
3  -H "Content-Type: application/json" \
4  -d '{
5    "model": "sentence-transformers/all-minilm-l6-v2",
6    "input": "RedPill protects model calls with hardware TEE"
7  }'

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