gliclass instruct large v1.0

An open zero-shot classifier that labels text with the categories you name. knowledgator/gliclass-instruct-large-v1.0

Get started Run in your cloud Hugging Face

Accuracy

76.9%

AG News, labelled correctly
Time per request

Not timed one request at a time
Serverless price

$0.19

per 1M tokens

Call it

from sie_sdk import SIEClient
client = SIEClient(api_key="sk-sie-…")
result = client.extract(
    "modelknowledgator/gliclass-instruct-large-v1.0",
    {"text": "textThe renewal charged me twice but the receipt link is broken."},
    labels=[
        "labelbilling",
        "labelbug report",
        "labelfeature request",
        "labelaccount access",
    ],
)
print(result["classifications"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const result = await client.extract(
  'knowledgator/gliclass-instruct-large-v1.0',
  { text: "The renewal charged me twice but the receipt link is broken." },
  { labels: ["billing","bug report","feature request","account access"] },
);
console.log(result.classifications);

curl https://api.superlinked.com/v1/extract/knowledgator%2Fgliclass-instruct-large-v1.0 \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"text\":\"The renewal charged me twice but the receipt link is broken.\"}],\"params\":{\"labels\":[\"billing\",\"bug report\",\"feature request\",\"account access\"]}}"

Build the "Classify" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: knowledgator/gliclass-instruct-large-v1.0 (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: a block of text plus a set of candidate labels.
- Behaviour: return the best-matching label(s) with native scores
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Next to the other zero-shot classifiers

Next to the other zero-shot classifiers
Model	AG News, labelled correctly	Time per request	Serverless price	Size
gliclass instruct large v1.0 knowledgator	76.9%	Not timed	$0.19 per 1M tokens	439M
GLiNER2.5 Decide fastino	Not measured	13.1 ms per message	$0.28 per 1M tokens	486M
DeBERTa v3 large zero-shot v2.0 MoritzLaurer	88.6%	Not timed	$0.129 per 1M tokens	435M
deberta v3 base zeroshot v2.0 MoritzLaurer	88.1%	Not timed	Run in your cloud	184M
ModernBERT base zeroshot v2.0 MoritzLaurer	84.3%	Not timed	Run in your cloud	150M

All 11 zero-shot classifiers

Measured on public datasets

Measured on public datasets
Dataset	Accuracy
AG News (decision)	80.8%
AG News	76.9%
Banking77	67.5%
emotion	51.5%

Facts

Size: 439M parameters
License: Apache-2.0
Reads: Text, up to 1,024 tokens
Endpoint: extract

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Time: SIE timed on a GPU server with the client alongside, one request at a time
Prices: Serverless list prices

Run gliclass instruct large v1.0 on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

Runs on your NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-default

Quickstart