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POST
/
responses
curl --request POST \ --url https://api.zerogpu.ai/v1/responses \ --header 'Content-Type: application/json' \ --header 'x-api-key: <api-key>' \ --header 'x-project-id: <api-key>' \ --data ' { "input": "The application is built with Python 3.11 and uses PostgreSQL 15 for storage. It runs on Kubernetes with Docker containers and communicates via gRPC.", "model": "gliner2-base-v1" } '
{}

Documentation Index

Fetch the complete documentation index at: https://docs.zerogpu.ai/llms.txt

Use this file to discover all available pages before exploring further.

GLiNER2 extends the original GLiNER architecture to support multi-task information extraction with a schema-driven interface. This base model provides efficient CPU-based inference while maintaining high accuracy across diverse extraction tasks. Unlike traditional NER models trained on fixed label sets, GLiNER2 accepts arbitrary entity types at inference time, making it adaptable to new domains without fine-tuning. It supports named entity recognition, relation extraction, and span classification within a unified framework, guided by natural language schema definitions.
References: Model docs • Terms • Privacy

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Specifications

PropertyValue
Model IDgliner2-base-v1
TaskPII
Typegliner2
Parameters205M
Version2
Max Tokens512
Input Price$0.20 / 1M
Output Price$0.40 / 1M
Total Price$0.60 / 1M

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Try it

Send a live request with your x-api-key and x-project-id. Model is fixed to gliner2-base-v1. Use request examples below to switch use cases (JSON extraction, NER, PII, and so on).

Authorizations

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x-api-key
string
header
required
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x-project-id
string
header
required

Body

application/json
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input
string<textarea>
required

Multi-line text or document content to send to the model.

Required string length: 1 - 131072
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instructions
string
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metadata
object

Response

Success

The response is of type object.