GPC-1

Introducing GPC-1, a general-purpose classifier based on Qwen3.5-35B-A3B, post-trained for categorical decisions, bounded numerical estimates, and image-conditioned coordinates.

Define your labels, numeric ranges, or allowed JSON records through one API. Build document routing, scoring, visual annotation, and structured workflows around outputs your application can use directly. Multiple numeric fields are evaluated in one model pass.

Demo · Get started · Download options · API guide · Local demo · Model card

Key features

• Categorical classification: supply your own labels and receive a selected class with probabilities over the available choices.
• Continuous range selections: specify bounds and a field description to obtain a numerical estimate.
• Image-conditioned numerical outputs: ask for coordinates using the image and a natural-language description of the target.
• Dependent structured choices: score complete allowed JSON records together to keep related categorical decisions consistent.
• OpenAI-style serving: use familiar messages and JSON schemas on NVIDIA hardware.

Continuous range selections

Define the range, units, and reference frame for each numeric field. The same interface can represent a position, angle, distance, or score.

For example, 0 to 1 can describe a horizontal image coordinate, while 0 to 360 can describe an angle in degrees.

{
  "key": "x_coord",
  "description": "Horizontal position of the requested point; 0 is the left edge and 1 is the right edge",
  "minimum": 0,
  "maximum": 1,
  "unit": "image-width fraction"
}

Each field uses 101 evenly spaced positions. The API returns the most likely position and a probability-weighted mean that can fall between positions. These are finite-resolution estimates. Output semantics.

Examples

Bounding boxes

Four normalized coordinates define two corners: x_min, y_min, x_max, and y_max, with the origin at the image’s top-left. Image request format.

Human pose and keypoints

Describe a landmark and request its coordinates, such as right_hip_x and right_hip_y. Each coordinate is a separate numeric estimate; a complete skeleton is not enforced.

Intent classification

Define intent labels in the request and receive a selected label with probabilities over those labels.

Numerical request

“A tank holds 200 liters. It is 35% full. Return the current volume of liquid in liters.”

Define volume over 0–200, with units liters. GPC-1 returns 70 as the most likely value. The same numeric interface can express a rating, distance, fraction, or image coordinate; the prompt supplies the interpretation.

Dependent decisions

Instead of independently choosing a department and an action, provide the complete records your workflow permits. GPC-1 scores those records together, so an invalid combination cannot appear in the returned support. Joint scoring is bounded by the records you enumerate, not arbitrary JSON generation.

See the API guide for request payloads and response fields.

Get started

Run GPC-1 on compatible NVIDIA hardware with enough memory for the BF16 model. The complete release includes the GPC-1 backbone, its matching adapter, and serving code. The server loads both weight components automatically.

python3 -m pip install "huggingface-hub>=1.7,<2"
hf download harshatheg/GPC-1 --local-dir gpc-1
python gpc-1/download.py verify gpc-1
cd gpc-1
python3 -m pip install --index-url https://download.pytorch.org/whl/cu130 torch==2.9.1 torchvision==0.24.1
python3 -m pip install -r requirements.txt
export GPC1_MODEL_PATH="$PWD/model"
export GPC1_API_KEY="$(python3 -c 'import secrets; print(secrets.token_urlsafe(32))')"
python3 -m uvicorn gpc1_server.api:app \
  --host 127.0.0.1 --port 8000 --no-access-log

Keep the server running. In another terminal, open gpc-1, set GPC1_API_KEY to the same key used by the server, and send an OpenAI-style request to /v1/chat/completions:

curl http://127.0.0.1:8000/v1/chat/completions \
  -H "Authorization: Bearer $GPC1_API_KEY" \
  -H "Content-Type: application/json" \
  --data @examples/atomic_request.json

The API uses familiar messages and response_format fields, with a gpc1 extension for the prediction mode. See serving and examples for numeric ranges, image inputs, joint records, and deployment configuration. The download guide covers pinned revisions, Python, CI, and offline deployment.

At a glance

Model detail	GPC-1
Backbone family	Qwen3.5-35B-A3B
Packaged weights	GPC-1 backbone + matching adapter
Architecture	Multimodal mixture of experts
Categorical choices	2–255 per request
Numeric outputs	101-point support over a range you define
Image interface	Image-conditioned numeric fields
Dependent outputs	Enumerated joint records
Server input ceiling	256K tokens (262,144), including compiled schema and image tokens
Serving	NVIDIA / PyTorch, OpenAI-style HTTP API
License	Apache-2.0

See context and deployment for hardware requirements and the hosted demo's separate limits.

Under the hood

GPC-1 uses schema-bound scoring to return typed predictions from its multimodal backbone and matching adapter. Numeric fields share one model pass; dependent categorical decisions are scored as complete allowed records.

Architecture · Model card · API documentation · Local demo · License · Notices

GPC-1 inference code is Apache-2.0. The Qwen base retains its upstream license and notices; third-party attribution is included with the package.