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
| # Runs ON the GCE instance — installs Docker, NVIDIA toolkit, builds stack | |
| set -euo pipefail | |
| echo "=== Installing Docker ===" | |
| curl -fsSL https://get.docker.com | sh | |
| sudo usermod -aG docker "$USER" | |
| echo "=== Installing NVIDIA Container Toolkit ===" | |
| curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \ | |
| sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg | |
| curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \ | |
| sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \ | |
| sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list | |
| sudo apt-get update | |
| sudo apt-get install -y nvidia-container-toolkit | |
| sudo nvidia-ctk runtime configure --runtime=docker | |
| sudo systemctl restart docker | |
| echo "=== Extracting code ===" | |
| mkdir -p ~/arcisvlm | |
| cd ~/arcisvlm | |
| tar xzf ~/arcisvlm-deploy.tar.gz | |
| echo "=== Downloading checkpoint from HuggingFace ===" | |
| pip3 install --quiet huggingface_hub | |
| python3 -c " | |
| from huggingface_hub import hf_hub_download | |
| import os | |
| os.makedirs('checkpoints', exist_ok=True) | |
| hf_hub_download('hardiksa/arcisvlm', 'v3_stage1_final.pt', local_dir='checkpoints/') | |
| print('Checkpoint downloaded successfully') | |
| " || echo "WARN: HF download failed — will need to copy checkpoint manually" | |
| echo "=== Building and starting Docker Compose ===" | |
| cd ~/arcisvlm | |
| sudo docker compose -f deploy/docker-compose.yml up -d --build | |
| echo "=== Waiting for services (30s) ===" | |
| sleep 30 | |
| curl -s http://localhost/health && echo "" || echo "WARN: Health check not yet responding" | |
| echo "=== Setup complete ===" | |
| sudo docker compose -f deploy/docker-compose.yml ps | |