Quickstart

Set up your API key

Create a Fastino account
Get your API key from Settings

Set your key as an environment variable so the examples below work as-is:

export FASTINO_API_KEY="your_api_key_here"

$env:FASTINO_API_KEY = "your_api_key_here"

Run your first inference call

Call POST /v1/systemone with a model, a state describing the situation, and one or more typed questions. GLiDE returns structured answers and calibrated probabilities directly - there’s no generated text to parse. The example below asks a single yes/no (noul) question. GLiDE costs $0.04 per 1 million input tokens. Output tokens are free ($0). See Pricing for the current inference rates.

cURL
Python
JavaScript

To run a GLiDE decision call:

curl -X POST "https://api.fastino.ai/v1/systemone" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fastino/GLiDE",
    "state": "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
    "questions": {
      "refund_allowed": {
        "type": "noul",
        "instructions": "Does this request qualify for a refund?",
        "criteria": { "true": "Qualifies", "false": "Does not qualify" }
      }
    }
  }'

To run a GLiDE decision call:

import os
import requests

response = requests.post(
    "https://api.fastino.ai/v1/systemone",
    headers={
        "X-API-Key": os.environ["FASTINO_API_KEY"],
        "Content-Type": "application/json",
    },
    json={
        "model": "fastino/GLiDE",
        "state": "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
        "questions": {
            "refund_allowed": {
                "type": "noul",
                "instructions": "Does this request qualify for a refund?",
                "criteria": {"true": "Qualifies", "false": "Does not qualify"},
            }
        },
    },
)

response.raise_for_status()
print(response.json())

To run a GLiDE decision call:

const response = await fetch(
  "https://api.fastino.ai/v1/systemone",
  {
    method: "POST",
    headers: {
      "X-API-Key": process.env.FASTINO_API_KEY,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "fastino/GLiDE",
      state: "Refund request: the receipt is attached, the purchase was 10 days ago, and refunds are allowed within 30 days.",
      questions: {
        refund_allowed: {
          type: "noul",
          instructions: "Does this request qualify for a refund?",
          criteria: { true: "Qualifies", false: "Does not qualify" },
        },
      },
    }),
  }
);

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

const result = await response.json();
console.log(JSON.stringify(result, null, 2));

Success looks like this:

{
  "model": "glide",
  "answers": {
    "refund_allowed": {
      "type": "noul",
      "noul": 0.99767683794902,
      "confidence": 0.9953536758980399
    }
  },
  "usage": { "input_tokens": 89, "output_tokens": 1 },
  "token_usage": 90
}

answers.refund_allowed.noul is the probability the answer is “yes” - 0.998 when rounded to three decimal places is a strong qualifying signal. Continue with the Noul, Choice, and Score references, or use the routing guide to turn confidence into an action.

Extracting entities instead? Use GLiNER 2.5

If you need span-level extraction (NER, classification, structured records) rather than a decision, call POST /v1/chat/completions with a model, a message, and a schema that defines what you want to extract:

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
  }'

For encoder models (GLiNER), the schema object defines the task and can include entities, classifications, structures, and relations. For decoder models (LLMs), omit schema and send a normal chat message. See the Inference API for the full contract, Python/JavaScript examples, and error codes.

Create a decoder training job

Create a LoRA job for interactive training (interactive job), where your application controls each training step: Query GET /v1/base-models?supports_training=true before creating the job and use an exact decoder model ID returned for your team. An interactive-training context limit describes that training operation; it can differ from the catalog model’s inference context or an inference endpoint’s request limit.

curl -X POST "https://api.fastino.ai/v1/training/jobs/create-model" \
  -H "X-API-Key: $FASTINO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "base_model": "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4",
    "lora_config": {"rank": 32}
  }'

The response contains the job_id and its create future_id. Continue with the Decoder Quickstart to run forward-backward and optimizer operations and save a checkpoint.

Next steps

Decision Models

Understand decision models and continue with either GLiDE or GLiNER 2.5 Decide.

GLiDE agent skill

Use this skill to help your coding agent classify, route, and score decisions with GLiDE.

GLiDE inference

Review the complete /v1/systemone request, response, limits, retries, and errors.

Training API

Train Nemotron 3.5 Super VL end-to-end with the Training API.

Get Started

Decision Models

Training API

GLiNER Models

Inference API

Troubleshooting

Models

Account

Set up your API key

Run your first inference call

Create a decoder training job

Next steps

Decision Models

GLiDE agent skill

GLiDE inference

Training API

​Set up your API key

​Run your first inference call

​Create a decoder training job

​Next steps

Decision Models

GLiDE agent skill

GLiDE inference

Training API

Set up your API key

Run your first inference call

Create a decoder training job

Next steps