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")# 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 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 | |
| ```bash | |
| 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: | |
| ```bash | |
| 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: | |
| ```python | |
| 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: | |
| ```bash | |
| 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: | |
| ```bash | |
| 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" | |
| ``` | |