Image-Text-to-Text
Transformers
Safetensors
PEFT
qwen3_5_moe
classification
structured-prediction
multimodal
lora
Instructions to use suryatmodulus/GPC-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# pip install -U transformers accelerate # 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
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96a4100 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | """Verify or copy the exact model bundled with this release."""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import shutil
from .assets import ASSET_ROOT,sha256_file
def main():
parser=argparse.ArgumentParser();parser.add_argument('--output',type=Path,required=True)
args=parser.parse_args();source=ASSET_ROOT.parent/'model'
inventory=json.loads((ASSET_ROOT/'base_files.sha256.json').read_text())
if args.output.resolve()!=source.resolve() and args.output.exists() and any(args.output.iterdir()):
raise RuntimeError('--output must be absent or empty')
for name,digest in inventory['files'].items():
if not (source/name).is_file() or sha256_file(source/name)!=digest:
raise RuntimeError(f'Bundled model identity mismatch: {name}')
if args.output.resolve()!=source.resolve():
args.output.mkdir(parents=True,exist_ok=True)
for name in inventory['files']:shutil.copyfile(source/name,args.output/name)
print(args.output.resolve())
if __name__=='__main__':main()
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