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 /DOWNLOADS.md

suryatmodulus

harshatheg

Duplicate from harshatheg/GPC-1

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Download GPC-1

Download the complete GPC-1 release: the GPC-1 backbone in model/, its matching adapter in adapter/, and the serving package. Keep both weight components and release metadata together; the server loads them automatically.

Command line

python -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

For a reproducible deployment, pin a commit SHA and use the same revision in every environment:

hf download harshatheg/GPC-1 --revision YOUR_COMMIT_SHA --local-dir gpc-1
python gpc-1/download.py verify gpc-1

Private or gated access uses HF_TOKEN in the environment. Keep the token in your secret manager; do not put it in command arguments or logs.

Python

If you already have download.py from the release package, run Python from its directory:

from pathlib import Path
from download import fetch

package = fetch(
    "harshatheg/GPC-1",
    revision="YOUR_COMMIT_SHA",
    local_dir=Path("gpc-1"),
    cache_dir=None,
    offline=False,
)
print(package["model"])

fetch uses huggingface_hub.snapshot_download for the complete repository. Verification checks that the root and packaged base configs match, all inventoried base files and indexed weight shards are present, and the runtime manifest, base metadata, and adapter hashes match. It does not hash every base weight shard; the server checks those hashes when it loads the model. No tracking endpoint is called.

Cache and offline use

After an online download, verify a copied package without contacting the Hub:

python gpc-1/download.py verify gpc-1

For offline deployment, download the complete package with --local-dir while online, then copy that directory to the offline machine. Run the verification command above on the copied directory. Verification makes no Hub request. Use the materialized package directory, not a cache snapshot containing symlinks.

CI

Pin the revision in your pipeline and keep the token in a CI secret. A complete package can then be mounted read-only by the serving process:

hf download harshatheg/GPC-1 --revision "$GPC1_REVISION" --local-dir gpc-1
python gpc-1/download.py verify gpc-1
export GPC1_MODEL_PATH="$PWD/gpc-1/model"