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
Download NOTICE from suryatmodulus/GPC-1: direct link, hf CLI and curl.
- Browser
- Download file 696 Bytes
-
https://huggingface.co/suryatmodulus/GPC-1/resolve/main/NOTICE
- Command line
-
hf download hf://suryatmodulus/GPC-1/NOTICE
-
curl -L -o NOTICE https://huggingface.co/suryatmodulus/GPC-1/resolve/main/NOTICE
696 Bytes
| GPC-1 | |
| This distribution includes original GPC-1 inference software licensed under | |
| the Apache License, Version 2.0. | |
| GPC-1 is designed for and may be distributed with an adapter for | |
| Qwen/Qwen3.5-35B-A3B at revision | |
| 59d61f3ce65a6d9863b86d2e96597125219dc754. The upstream Qwen model is | |
| licensed under the Apache License, Version 2.0. Preserve all applicable | |
| upstream copyright, patent, trademark, and attribution notices. | |
| Qwen model source: https://huggingface.co/Qwen/Qwen3.5-35B-A3B | |
| Pinned license: https://huggingface.co/Qwen/Qwen3.5-35B-A3B/blob/59d61f3ce65a6d9863b86d2e96597125219dc754/LICENSE | |
| Third-party attributions and example-media licenses are documented in | |
| docs/DATA_AND_LICENSES.md. | |