Apex-core

Evidence in, decisions out. Explore SCX Apex, a family of GLiClass 4 based Large Decision Models.

A 2B Large Decision Model for everyday text and image decisions on a single GPU.

Apex-core reads text and images alongside instructions, policies, and candidate answers. Use it for everyday routing, triage, document and screenshot classification, and choosing the next action in your application.

Part of the SCX Apex family, based on GLiClass 4. Give the model evidence, an instruction, and the answers you would accept. It scores the candidate answers in one forward pass without generating text.

Property	Value
Parameters	Approximately 2B
Inputs	Text and images
Backbone	Qwen/Qwen3.5-2B
Framework	GLiClass
License	Apache 2.0
Scores	Uncalibrated; tune thresholds on representative data

Use an LLM to write. Use Apex to decide.

• Change the policy, not the model. Supply instructions, policies, or rubrics at inference time, and change candidate answers as your workflow evolves.
• Set thresholds for your workflow. This release provides uncalibrated scores, which can cluster near 0 or 1. Evaluate thresholds on representative data before automating decisions or escalating cases to a person or an LLM.
• Make decisions directly. Get structured label scores for routing, triage, moderation, ranking, extraction decisions, judging, and selecting the next step.
• Bring images and conversations into the decision. Score text alongside screenshots, receipts, charts, or camera frames. The GLiClass decoder-KV architecture also supports text-only streaming classification with a per-session cache. See the upstream streaming examples.

Choose an Apex model

Model	Size	Inputs	Best suited to
Apex-flash	0.4B	Text	High-volume decisions on CPU or GPU
Apex-core	2B	Text and images	Everyday decisions on a single GPU
Apex-large	9B	Text and images	Knowledge-heavy and visual decisions

Explore the Apex family collection. All three releases provide open weights under Apache 2.0. Apex-core's scores are uncalibrated; evaluate thresholds for each model on your own data.

Quick start

Use Python 3.10+ and an up-to-date GLiClass installation:

pip install -U gliclass

The example uses a GPU with BF16 support. The checkpoint weights occupy approximately 4.7 GB; allow additional memory for inference.

import torch
from gliclass import GLiClassModel, ZeroShotClassificationPipeline

model_id = "scx-admin/Apex-core"
device = "cuda:0"
model = GLiClassModel.from_pretrained(model_id, torch_dtype=torch.bfloat16)
pipeline = ZeroShotClassificationPipeline(
    model,
    model_id,
    classification_type="multi-label",
    max_length=8192,
    device=device,
)

results = pipeline(
    "My parcel has not arrived. Please check the delivery status.",
    ["delivery issue", "refund request", "technical support"],
    threshold=0.5,
)[0]
for result in results:
    print(result["label"], result["score"])

Pass prompt="your instruction or policy" to guide the decision. Use classification_type="single-label" for mutually exclusive choices. Multi-label results may contain more than one label or no labels above the threshold.

Images

Use the same pipeline with an image path, URL, or PIL image:

results = pipeline(
    ["Which fruit is shown?"],
    ["strawberry", "lime", "fig", "peach"],
    images=[["fruit.jpg"]],
    classification_type="single-label",
)[0]
print(results)

Limitations

Predictions depend on label wording, instructions, domain, input length, and thresholds. English is the primary language. The upstream release reports that its scores are uncalibrated: a score of 0.9 does not imply 90% accuracy. Evaluate scores and thresholds on representative data. See the upstream model card for training details, evaluations, and further limitations.

Attribution and license

This release republishes the upstream checkpoint under the Apex name. The weights, configuration, tokenizer, and other inference assets are unchanged; no additional fine-tuning is claimed.

Based on GLiClass 4, developed by Knowledgator. Original checkpoint: knowledgator/gliclass-v4-2b, revision 40fe023b45e8d97d1ae4f7f9342279ac1c76317d. Distributed under the upstream Apache License 2.0; see LICENSE. The original model card is preserved in UPSTREAM_README.md and at the upstream link.

Related work: GLiClass and SCX Router.