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 / docs /ARCHITECTURE.md

suryatmodulus

harshatheg

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	# Architecture

	GPC-1 uses the Qwen3.5-35B-A3B multimodal mixture-of-experts architecture with schema-bound inference. You define the output space; the server scores it and returns typed values.

	## Model package

	`model/` contains GPC-1's modified backbone weights; `adapter/` contains the matching adapter applied at load time. Use both components from the same release. Qwen identifies the upstream architecture; the packaged weights belong to GPC-1.

	The server loads the backbone in BF16 and the adapter in FP32. It uses the existing language-model output projection to score the request's allowed outputs.

	\| Mode \| Output \|
	\| --- \| --- \|
	\| Categorical \| A selected label and probabilities over your choices \|
	\| Numeric \| A grid estimate, probability-weighted mean, and distribution per field \|
	\| Image numeric \| Numeric fields conditioned on one image \|
	\| Finite joint \| A selected complete record and probabilities over allowed records \|

	## Numeric ranges

	For bounds `[minimum, maximum]`, the 101-point grid is:

	`value(i) = minimum + (maximum - minimum) × i / 100`

	Numeric fields share one model pass. Each returns a marginal distribution; shared context does not make them a full joint distribution. The probability-weighted mean can fall between grid positions. Angles and other circular quantities need application-specific interpretation.

	## Dependent records

	Finite-joint mode scores complete records rather than choosing their fields independently. Probabilities are conditional on the supplied records. Compute and memory grow with the number and length of candidates.

	## Serving

	The runtime verifies packaged asset identities, uses request-local execution, and rejects over-limit inputs without truncation. Its input ceiling is 256K tokens (262,144), including compiled request overhead. See [context configuration](../API.md#context-window) for deployment requirements. Preserve the supplied precision, prompt formatting, and token mapping when reproducing outputs. Scale concurrent traffic with separate replicas.

	See the [API guide](../API.md) for request formats and limits.