Instructions to use suryatmodulus/GPC-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use suryatmodulus/GPC-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="suryatmodulus/GPC-1")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("suryatmodulus/GPC-1")
model = AutoModelForMultimodalLM.from_pretrained("suryatmodulus/GPC-1", device_map="auto")

PEFT
How to use suryatmodulus/GPC-1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use suryatmodulus/GPC-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "suryatmodulus/GPC-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/suryatmodulus/GPC-1

SGLang

How to use suryatmodulus/GPC-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "suryatmodulus/GPC-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "suryatmodulus/GPC-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "suryatmodulus/GPC-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use suryatmodulus/GPC-1 with Docker Model Runner:
```
docker model run hf.co/suryatmodulus/GPC-1
```

GPC-1 / API.md

suryatmodulus

harshatheg

Duplicate from harshatheg/GPC-1

96a4100 about 11 hours ago

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GPC-1 API

The NVIDIA server exposes GET /v1/models and POST /v1/chat/completions. Requests use OpenAI-style messages and response_format; responses contain choices and a gpc1 result object. This is a structured-prediction interface, not a complete Chat Completions implementation.

Requests and responses

Use model: "gpc-1" and a strict JSON schema:

{"type":"json_schema","json_schema":{"name":"result","strict":true,"schema":{}}}

Replace schema with an explicit object schema. Require every property and set additionalProperties: false. Supported types are object, array, string, number, integer, boolean, and null. References, unions, recursion, and patterns are unsupported.

Put mode-specific options in gpc1. OpenAI Python clients can pass them through extra_body={"gpc1": ...}.

choices[0].message.content: JSON-encoded selected values.
gpc1: probability distributions, numeric summaries, and execution accounting.
usage: null; token usage is not reported.
Errors: {"error":{"message": "...", "type": "...", "param": null, "code": "..."}}.

Interpreting scores

Probabilities describe the model's distribution over the choices, numeric grid, or complete records supplied in the request. Changing that output space can change the scores. A probability of 0.9 is not a measured 90% accuracy rate; choose decision thresholds using representative examples from your application.

Atomic categorical

Set mode: "atomic", field_key, and an ordered choices array of 2–255 unique JSON values. The schema must contain exactly that field with a matching enum.

The response includes value, selected_index, and probabilities in choice order.

Complete categorical request

Numeric101

Set mode: "numeric101" and numeric_fields. Each field requires key, description, minimum, maximum, and unit. Optional aliases and reference clarify meaning. Schema properties must use type: "number" with matching bounds.

Each field uses value(i) = minimum + (maximum - minimum) × i / 100, for i = 0…100.

Response field	Meaning
`choices[0].message.content`	Most likely grid value for each key
`gpc1.fields[key].map_value`	Most likely grid value
`gpc1.fields[key].expected_value`	Probability-weighted mean in the declared units
`gpc1.fields[key].probabilities`	Probabilities in ascending grid order
`gpc1.fields[key].normalized_grid`	Grid positions from 0 to 1

The mean may fall between grid positions. Numeric fields are separate marginals, not a full joint distribution.

Complete numeric request

Finite joint

Set mode: "finite_joint" and allowed_records containing unique complete JSON records that satisfy the schema. The response includes the selected value, candidate probabilities, and log_scores.

The schema accepts 2–255 records, subject to stricter runtime memory and row limits. The default runtime limit is 32 records. Probabilities are conditional on the supplied records; this mode does not generate arbitrary JSON.

Images

Include one image_url content part containing a base64 PNG, JPEG, or WebP data URI:

{"type":"image_url","image_url":{"url":"data:image/jpeg;base64,..."}}

Images are supported only in numeric101. HTTP(S) and file URLs are rejected. Describe the target, coordinate origin, axes, and units explicitly. For boxes, request x_min, y_min, x_max, and y_max; multiply normalized horizontal values by image width and vertical values by image height. Check coordinate ordering before drawing.

The visual README examples use expected_value, not the MAP values in message content.

Deployment

Set GPC1_MODEL_PATH to the packaged model/ directory and GPC1_API_KEY to a secret. Send Authorization: Bearer YOUR_KEY to the model endpoints. Startup verifies the packaged base and adapter; serving does not fetch models or images from URLs.

Run one server worker per model on a GPU. Model requests execute one at a time; additional requests wait in a bounded queue. Use separate replicas to serve parallel traffic. GET /healthz returns {"status":"ok"}. GET /readyz returns {"status":"ready"} after the model backend is selected, or HTTP 503 with {"status":"not_ready"} before then. These two health endpoints do not require the bearer key and do not return model metadata.

Build the container from the downloaded package, then mount its model weights read-only. Set GPC1_API_KEY in your environment before launching:

docker build -t gpc-1 .
docker run --gpus all --rm -p 127.0.0.1:8000:8000 \
  -e GPC1_API_KEY -e GPC1_MODEL_PATH=/models/gpc-1 \
  -v "$PWD/model:/models/gpc-1:ro" gpc-1

For internet-facing deployment, put an HTTPS proxy in front of the server. Keep the bearer key server-side and configure request-size limits at the proxy too.

Context window

The release server admits up to 256K tokens (262,144) per compiled input, matching the backbone's configured context capacity. This includes prompt formatting, schema, choices, numeric field definitions, and image tokens, not just the supplied text. Inputs are never silently truncated.

This is the configured ceiling, not a completed 256K GPU validation or a guarantee that every request fits on one GPU. Memory and latency depend on input length, images, and candidate count. Set GPC1_MAX_INPUT_TOKENS lower for your hardware. The hosted ZeroGPU demo uses a separate 8,192-token limit.

Finite-joint requests also have an aggregate padded-token budget, defaulting to twice the input ceiling (524,288 tokens). Larger candidate sets may reach that budget before the per-input ceiling. Request-body and output-memory limits remain independent.

Setting	Default
`GPC1_MAX_INPUT_TOKENS`	262,144; configurable from 1 to 262,144
`GPC1_MAX_CONCURRENCY`	1, required; use replicas for parallel traffic
`GPC1_MAX_PENDING_REQUESTS`	4 waiting requests; one additional request may be active
`GPC1_MAX_QUEUE_WAIT_SECONDS`	2
`GPC1_MAX_BODY_BYTES`	16 MiB
`GPC1_MAX_IMAGE_BYTES`	3 MiB
`GPC1_MAX_IMAGE_PIXELS`	16,777,216
`GPC1_IMAGE_CACHE_MB`	64; one image's vision features, or 0 to disable
`GPC1_MAX_JOINT_ROWS`	32
`GPC1_MAX_PADDED_TOKENS`	`max(131072, 2 × GPC1_MAX_INPUT_TOKENS)`; 524,288 by default
`GPC1_MAX_LOGIT_VECTORS`	512
`GPC1_MAX_LOGITS_BYTES`	512 MiB

Over-limit inputs are rejected, never truncated. POST requests require Content-Length. A full queue or an expired queue wait returns HTTP 429 with code server_busy; body and image size limits return 413. Set GPC1_ADMIT_VISION_NUMERIC=disabled or GPC1_ADMIT_FINITE_JOINT=disabled to disable those modes.

Repeated requests for the same processed image can reuse its vision features without changing the prediction math. A new image still requires encoding. The cache stays in memory, holds at most one image's features, and can be disabled with GPC1_IMAGE_CACHE_MB=0.

Unsupported

Streaming, tools, audio, remote image fetching, atomic image classification, image-conditioned finite-joint output, and unrestricted JSON generation. Omit temperature and max_completion_tokens; they are rejected rather than ignored.

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