zlm-v1-signal-extract: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"model": "zlm-v1-signal-extract",
"input": "How can I pay using my credit card?"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"model": "zlm-v1-signal-extract",
"input": "How can I pay using my credit card?"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: 'zlm-v1-signal-extract', input: 'How can I pay using my credit card?'})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"model\": \"zlm-v1-signal-extract\",\n \"input\": \"How can I pay using my credit card?\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"zlm-v1-signal-extract\",\n \"input\": \"How can I pay using my credit card?\"\n}"
response = http.request(request)
puts response.read_body{
"audience": [
{
"name": "Technology & Computing",
"score": 0.7686
},
{
"name": "Consumer Electronics",
"score": 0.7206
},
{
"name": "65-69",
"score": 0.595
}
],
"content": {
"iab_1_0": [
{
"name": "Cell Phones",
"score": 0.7597
},
{
"name": "Technology & Computing",
"score": 0.7387
}
],
"iab_2_2": [
{
"name": "Smartphones",
"score": 0.7597
},
{
"name": "Wearable Technology",
"score": 0.7387
},
{
"name": "Technology & Computing",
"score": 0.7109
}
]
}
}{}By model
zlm-v1-signal-extract
Model details for zlm-v1-signal-extract. Topics, keywords, and intent extracted from text in a single call.
POST
/
responses
zlm-v1-signal-extract: Responses
curl --request POST \
--url https://api.zerogpu.ai/v1/responses \
--header 'Content-Type: application/json' \
--header 'x-api-key: <api-key>' \
--data '
{
"model": "zlm-v1-signal-extract",
"input": "How can I pay using my credit card?"
}
'import requests
url = "https://api.zerogpu.ai/v1/responses"
payload = {
"model": "zlm-v1-signal-extract",
"input": "How can I pay using my credit card?"
}
headers = {
"x-api-key": "<api-key>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
body: JSON.stringify({model: 'zlm-v1-signal-extract', input: 'How can I pay using my credit card?'})
};
fetch('https://api.zerogpu.ai/v1/responses', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));falsepackage main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.zerogpu.ai/v1/responses"
payload := strings.NewReader("{\n \"model\": \"zlm-v1-signal-extract\",\n \"input\": \"How can I pay using my credit card?\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("x-api-key", "<api-key>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}require 'uri'
require 'net/http'
url = URI("https://api.zerogpu.ai/v1/responses")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model\": \"zlm-v1-signal-extract\",\n \"input\": \"How can I pay using my credit card?\"\n}"
response = http.request(request)
puts response.read_body{
"audience": [
{
"name": "Technology & Computing",
"score": 0.7686
},
{
"name": "Consumer Electronics",
"score": 0.7206
},
{
"name": "65-69",
"score": 0.595
}
],
"content": {
"iab_1_0": [
{
"name": "Cell Phones",
"score": 0.7597
},
{
"name": "Technology & Computing",
"score": 0.7387
}
],
"iab_2_2": [
{
"name": "Smartphones",
"score": 0.7597
},
{
"name": "Wearable Technology",
"score": 0.7387
},
{
"name": "Technology & Computing",
"score": 0.7109
}
]
}
}{}ZeroGPU’s signal extractor turns unstructured text into structured signals that downstream systems can act on. One inference call returns topics, keywords, intent, and other contextual attributes as structured output, so content enrichment, contextual intelligence, ad targeting, agent routing, recommendation systems, and analytics pipelines all read from the same pass. At 80M parameters it is built for high-volume workloads where a general-purpose LLM is more machinery than the job needs.References: Terms • Privacy
Authorizations
Headers
Optional project identifier. Scopes the request to a specific project when provided.
Body
application/json
Model identifier (fixed for this playground). Use request examples to change use cases.
Allowed value:
"zlm-v1-signal-extract"Example:
"zlm-v1-signal-extract"
Multi-line text or document content to send to the model.
Required string length:
1 - 131072Response
Success
The response is of type object.

