API-Referenz

curl https://api.fastino.ai/v1/base-models \
  -H "X-API-Key: $FASTINO_API_KEY"

import os
import requests

response = requests.get(
    "https://api.fastino.ai/v1/base-models",
    headers={"X-API-Key": os.environ["FASTINO_API_KEY"]},
)
response.raise_for_status()
print(response.json())

const response = await fetch(
  "https://api.fastino.ai/v1/base-models",
  {
    headers: {
      "X-API-Key": process.env.FASTINO_API_KEY
    }
  }
);

if (!response.ok) {
  throw new Error(`Model request failed: ${response.status}`);
}

console.log(await response.json());

curl -X POST "https://api.fastino.ai/v1/chat/completions" \
  -H "Authorization: Bearer $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fastino/gliner2.5-multi-v1",
    "messages": [
      {
        "role": "user",
        "content": "Apple announced the MacBook Pro at WWDC in Cupertino."
      }
    ],
    "schema": {
      "entities": [
        {
          "name": "organization",
          "description": "business or institution name"
        },
        {
          "name": "product",
          "description": "named commercial product"
        },
        {
          "name": "event",
          "description": "named conference or event"
        },
        {
          "name": "location",
          "description": "city, region, or place"
        }
      ]
    },
    "threshold": 0.5
  }'

import json
import os

import requests

response = requests.post(
    "https://api.fastino.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {os.environ['FASTINO_API_KEY']}",
        "Content-Type": "application/json",
    },
    json={
        "model": "fastino/gliner2.5-multi-v1",
        "messages": [
            {
                "role": "user",
                "content": "Apple announced the MacBook Pro at WWDC in Cupertino.",
            }
        ],
        "schema": {
            "entities": [
                {"name": "organization", "description": "business or institution name"},
                {"name": "product", "description": "named commercial product"},
                {"name": "event", "description": "named conference or event"},
                {"name": "location", "description": "city, region, or place"},
            ]
        },
        "threshold": 0.5,
    },
)
response.raise_for_status()

result = response.json()
entities = json.loads(result["choices"][0]["message"]["content"])
print(json.dumps(entities, indent=2))

const response = await fetch(
  "https://api.fastino.ai/v1/chat/completions",
  {
    method: "POST",
    headers: {
      Authorization: `Bearer ${process.env.FASTINO_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "fastino/gliner2.5-multi-v1",
      messages: [
        {
          role: "user",
          content: "Apple announced the MacBook Pro at WWDC in Cupertino.",
        },
      ],
      schema: {
        entities: [
          { name: "organization", description: "business or institution name" },
          { name: "product", description: "named commercial product" },
          { name: "event", description: "named conference or event" },
          { name: "location", description: "city, region, or place" },
        ],
      },
      threshold: 0.5,
    }),
  }
);

if (!response.ok) {
  throw new Error(`Inference failed: ${response.status} ${await response.text()}`);
}

const result = await response.json();
const entities = JSON.parse(result.choices[0].message.content);
console.log(JSON.stringify(entities, null, 2));

curl -X POST https://api.fastino.ai/v1/training/jobs \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "my-gliner-model",
    "base_model": "fastino/gliner2-base-v1",
    "datasets": [{"name": "my-dataset"}],
    "training_type": "lora",
    "nr_epochs": 5,
    "learning_rate": 5e-5
  }'

import os
import requests

response = requests.post(
    "https://api.fastino.ai/v1/training/jobs",
    headers={
        "X-API-Key": os.environ["FASTINO_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "model_name": "my-gliner-model",
        "base_model": "fastino/gliner2-base-v1",
        "datasets": [{"name": "my-dataset"}],
        "training_type": "lora",
        "nr_epochs": 5,
        "learning_rate": 5e-5,
    },
)
response.raise_for_status()
print(response.json())