Image-Text-to-Text
Transformers
English
vision-language-model
vlm
surveillance
iot
gemma
vl-jepa
multimodal
object-detection
video-analytics
Instructions to use hardiksa/arcisvlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hardiksa/arcisvlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hardiksa/arcisvlm")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hardiksa/arcisvlm", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hardiksa/arcisvlm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hardiksa/arcisvlm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hardiksa/arcisvlm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hardiksa/arcisvlm
- SGLang
How to use hardiksa/arcisvlm 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 "hardiksa/arcisvlm" \ --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": "hardiksa/arcisvlm", "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 "hardiksa/arcisvlm" \ --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": "hardiksa/arcisvlm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hardiksa/arcisvlm with Docker Model Runner:
docker model run hf.co/hardiksa/arcisvlm
| #!/usr/bin/env python3 | |
| """Push a checkpoint to HuggingFace.""" | |
| import os | |
| import sys | |
| import glob | |
| def main(): | |
| if len(sys.argv) < 3: | |
| print("Usage: push_to_hf.py <local_filename> <hf_filename>") | |
| sys.exit(1) | |
| local_name = sys.argv[1] | |
| hf_name = sys.argv[2] | |
| from huggingface_hub import HfApi | |
| api = HfApi(token=os.environ.get("HF_TOKEN")) | |
| # Search for the file | |
| matches = glob.glob(f"checkpoints/{local_name}") | |
| if not matches: | |
| matches = glob.glob(f"checkpoints/*{local_name}*") | |
| if not matches: | |
| print(f"No checkpoint matching '{local_name}' found in checkpoints/") | |
| sys.exit(1) | |
| for f in matches: | |
| size_mb = os.path.getsize(f) / (1024 * 1024) | |
| if size_mb < 1: | |
| print(f"Skipping {f} — too small ({size_mb:.1f} MB), likely corrupted") | |
| continue | |
| print(f"Uploading {f} ({size_mb:.0f} MB) -> {hf_name}") | |
| api.upload_file( | |
| path_or_fileobj=f, | |
| path_in_repo=f"checkpoints/{hf_name}", | |
| repo_id="hardiksa/arcisvlm", | |
| repo_type="model", | |
| ) | |
| print("Done.") | |
| if __name__ == "__main__": | |
| main() | |