Apex-large

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

A 9B Large Decision Model for knowledge-heavy text and image decisions.

Apex-large reads evidence and candidate answers together, using instructions, policies, or rubrics to guide its scores. Use it for more demanding classification, reasoning, and visual decision workflows.

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 9B
Inputs	Text and images
Backbone	Qwen/Qwen3.5-9B
Framework	GLiClass
License	Apache 2.0

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.
• Act on confidence. Use calibrated label scores to automate confident decisions and escalate uncertain cases to a person or an LLM. Validate thresholds on your own data.
• 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 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-large	9B	Text and images	Knowledge-heavy and visual decisions

Explore the Apex family collection. Both releases provide open weights under Apache 2.0.

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 BF16 weights alone occupy approximately 18 GB; allow additional memory for inference.

import torch
from gliclass import GLiClassModel, ZeroShotClassificationPipeline

model_id = "scx-admin/Apex-large"
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. Calibration can drift on new domains; 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-v4-9b, revision 14d11a92b5e0bab4a166e1fce4cc0777cac27182. 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.