Image-Text-to-Text
Transformers
Safetensors
PEFT
qwen3_5_moe
classification
structured-prediction
multimodal
lora
Instructions to use harshatheg/GPC-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use harshatheg/GPC-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="harshatheg/GPC-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("harshatheg/GPC-1") model = AutoModelForMultimodalLM.from_pretrained("harshatheg/GPC-1", device_map="auto") - PEFT
How to use harshatheg/GPC-1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use harshatheg/GPC-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "harshatheg/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": "harshatheg/GPC-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/harshatheg/GPC-1
- SGLang
How to use harshatheg/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 "harshatheg/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": "harshatheg/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 "harshatheg/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": "harshatheg/GPC-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use harshatheg/GPC-1 with Docker Model Runner:
docker model run hf.co/harshatheg/GPC-1
Download assets/base_files.sha256.json from harshatheg/GPC-1: direct link, hf CLI and curl.
- Browser
- Download file 2.77 kB
-
https://huggingface.co/harshatheg/GPC-1/resolve/main/assets/base_files.sha256.json
- Command line
-
hf download hf://harshatheg/GPC-1/assets/base_files.sha256.json
-
curl -L -o base_files.sha256.json https://huggingface.co/harshatheg/GPC-1/resolve/main/assets/base_files.sha256.json
2.77 kB
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| "video_preprocessor_config.json": "7768af27c1fafa9cc9011c1dc20067e03f8915e03b63504550e11d5066986d13", | |
| "vocab.json": "ce99b4cb2983d118806ce0a8b777a35b093e2000a503ebde25853284c9dfa003" | |
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| "model_id": "harshatheg/GPC-1", | |
| "revision": "base-278124e95f96d21b" | |
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