HazırGPU TEE

Sentence Transformers: all-MiniLM-L6-v2

Model Kimliğisentence-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.

Geliştirmeye başlayın Dokümanlar

girdi

$0.0050/M

çıktı

Free/M

bağlam

512

oluşturuldu

25 Kas 2025

Desteklenen API yapısı

girdi

text · embeddings

çıktı

embeddings

araçlar

Listelenmiyor

json modu

Listelenmiyor

Doğrulama

imza

yanıt kimliği

attestasyon

GPU TEE

sağlayıcı

Phala

Provider

Phala

GPU TEE

girdi

$0.0050/M

çıktı

Free/M

bağlam

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  }'

Daha fazla model

Diğer özel çıkarım rotaları.

Kataloğu görüntüle

şifreli

Phala: Gemma-4 26B-A4B Uncensored (Heretic)

Uncensored "Heretic" variant of google/gemma-4-26B-A4B-it created using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method and row-norm preservation. Refusals drop from 100/100 to 11/100 with KL divergence 0.0499 vs the base model. The base Gemma 4 26B A4B is a Mixture-of-Experts model with 25.2B total / 3.8B active parameters (8 active / 128 total experts), 30-layer transformer with hybrid local sliding (1024) + global attention, supporting a 256K context window. Natively multimodal (text + images, variable aspect ratios). Strong on coding, reasoning, function calling, with native system prompt support across 35+ languages. Served on Phala in TDX-attested H200 enclave with end-to-end ECDSA response signing; vLLM-compatible FP8-Static quantization by cloud19 (router excluded from quantization).

bağlam

66K

girdi

$0.15/M

şifreli

Phala: Qwen3.6 35B-A3B Uncensored (Aggressive)

Uncensored "Aggressive" variant of Qwen3.6-35B-A3B from Alibaba's Qwen team. The fine-tune by HauhauCS removes refusal behaviors (0/465 refusals) without modifying datasets or core capabilities. The base architecture is a 35B-parameter Mixture-of-Experts model with 256 experts routing 8 per token (~3B active params), 40 layers, and a hybrid linear+full-softmax attention mechanism (3:1 ratio). Supports a native 262K context and is natively multimodal across text, images, and video. Served on Phala in TDX-attested H200 enclave with end-to-end ECDSA response signing; FP8 quantization by lamianlbe.

bağlam

131K

girdi

$0.30/M

şifreli

Qwen: Qwen3.5-27B

The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of the Qwen3.5-122B-A10B.

bağlam

262K

girdi

$0.30/M

şifreli

Z.AI: GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

bağlam

203K

girdi

$0.10/M