GLiDE

#1 decision model in the world

GLiDE is ranked #1 of 229 models on the Decision Index 0.4.1 (Thursday, 22 October 2026).

Inference provider: Fastino — use GLiDE through Fastino's hosted API (POST https://api.fastino.ai/v1/systemone, model fastino/GLiDE). Get an API key · API documentation

GLiDE is a decision model from Fastino. Given a state (text or JSON, optionally with images) and one or more typed questions, it returns a probability for every option of every question.

Fastino API

Use GLiDE through Fastino’s hosted API for classification, routing, and scoring. Send decision requests to POST https://api.fastino.ai/v1/systemone with model fastino/GLiDE. The instructions below cover running this repository locally.

Overview

• Typed decisions: choice (one of up to 255 named options), noul (yes / no, returns P(yes)) and score (ordered levels).
• Probabilities for every option, read from a 255-way decision readout. Nothing is generated.
• One forward pass per question. All questions of a request (and of concurrent requests) are scored together in micro-batches.
• Text, JSON and images: the state can be a string, any JSON value, or a list of text and image parts.
• Long contexts: up to 40,000 tokens per question prompt by default (state, question, options and image tokens).
• Interfaces: a Python API and an HTTP server speaking the System One format (POST /v1/systemone).

Hardware and memory

Weights are copied to the GPU tensor by tensor, so host memory use during loading stays low. Image preprocessing runs on the CPU.

Install

pip install huggingface_hub
hf download fastino/GLiDE --local-dir GLiDE    # needs a token with read access to the fastino org
cd GLiDE
pip install -r requirements.txt

• Tested with Python 3.12, torch==2.14.0 and transformers==5.17.0 (all pins are in requirements.txt).
• flash-linear-attention==0.5.2 is optional but recommended: it provides fast kernels for the linear-attention layers. Without it transformers falls back to a much slower PyTorch implementation, which was not part of the validation. All numbers below were measured with it installed.
• At load, transformers logs that causal_conv1d is not installed and uses its PyTorch path. This is expected: the validated setup does not use that package.
• The glide/ package lives at the repository root: run from the repository directory or add it to PYTHONPATH.

Quickstart

Python

from glide import Glide

model = Glide.from_pretrained(
    "GLiDE"
)  # local directory, or the Hub repo id "fastino/GLiDE"

result = model.decide(
    state={
        "ticket": "I was charged twice for order #4821 and the second charge is still pending.",
        "customer_tier": "gold",
    },
    questions={
        "intent": {
            "type": "choice",
            "instructions": "What does the customer want?",
            "criteria": {
                "refund": "Get money back",
                "cancel": "Cancel the order",
                "track": "Track a delivery",
                "other": "Something else",
            },
        },
        "urgent": {
            "type": "noul",
            "instructions": "Should this ticket be escalated today?",
        },
        "priority": {
            "type": "score",
            "instructions": "What support priority should this ticket get?",
            "criteria": [
                "low: no impact on the customer",
                "medium: the customer is inconvenienced",
                "high: the customer has lost money or cannot use the service",
            ],
        },
    },
)

Result on an RTX PRO 6000 (rounded):

{
  "model": "glide-v1.2",
  "answers": {
    "intent": {"type": "choice", "choice": "refund", "confidence": 0.9521, "probabilities": {"refund": 0.9751, "cancel": 0.0016, "track": 0.0004, "other": 0.0229}},
    "urgent": {"type": "noul", "noul": 0.8267, "confidence": 0.6534},
    "priority": {"type": "score", "score": 2, "expected_level": 1.9191, "confidence": 0.8527, "probabilities": {"0": 0.0048, "1": 0.0712, "2": 0.9239}}
  },
  "usage": {"input_tokens": 316, "output_tokens": 0}
}

With images (PIL images, file paths, bytes, base64 strings or data URLs):

from PIL import Image, ImageDraw

img = Image.new("RGB", (512, 512), "white")
ImageDraw.Draw(img).ellipse((128, 128, 384, 384), fill="red")

result = model.decide(
    state="A product photo from the catalogue.",
    questions={
        "color": {
            "type": "choice",
            "instructions": "What color is the shape in the image?",
            "criteria": {"red": "Red", "green": "Green", "blue": "Blue"},
        },
        "circle": {"type": "noul", "instructions": "Is the shape a circle?"},
    },
    images=[img],
)

{
  "model": "glide-v1.2",
  "answers": {
    "color": {"type": "choice", "choice": "red", "confidence": 0.9997, "probabilities": {"red": 0.9998, "green": 0.0001, "blue": 0.0001}},
    "circle": {"type": "noul", "noul": 0.9941, "confidence": 0.9882}
  },
  "usage": {"input_tokens": 653, "output_tokens": 0}
}

model.decide_batch([{"state": ..., "questions": ...}, ...]) scores several requests together and returns o