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
| # Setup script for vast.ai GPU instance (8x A100 80GB) | |
| # Run this after SSHing into a fresh vast.ai instance | |
| set -e | |
| echo "=========================================" | |
| echo "ArcisVLM Training Environment Setup" | |
| echo "=========================================" | |
| # Install system dependencies | |
| echo "=== Installing system packages ===" | |
| apt-get update -qq | |
| apt-get install -y -qq git git-lfs htop tmux 2>&1 | tail -3 | |
| # Install Python packages | |
| echo "=== Installing Python packages ===" | |
| pip install --upgrade pip -q | |
| pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121 -q 2>&1 | tail -3 | |
| pip install datasets transformers pillow pyyaml tqdm -q 2>&1 | tail -3 | |
| # Setup Git LFS | |
| git lfs install | |
| # Clone repo (token should be set as GH_TOKEN env var) | |
| echo "=== Cloning repository ===" | |
| if [ -d "/workspace/arcisvlm" ]; then | |
| echo "Repo already exists, pulling latest..." | |
| cd /workspace/arcisvlm && git pull | |
| else | |
| if [ -n "$GH_TOKEN" ]; then | |
| git clone https://x-access-token:${GH_TOKEN}@github.com/hardiksa/arcisvlm.git /workspace/arcisvlm | |
| else | |
| git clone https://github.com/hardiksa/arcisvlm.git /workspace/arcisvlm | |
| fi | |
| fi | |
| cd /workspace/arcisvlm | |
| # Pull LFS files (existing checkpoints) | |
| echo "=== Pulling LFS files ===" | |
| git lfs pull | |
| # Download training datasets | |
| echo "=== Downloading datasets ===" | |
| python3 scripts/download_datasets.py --config configs/scale_1.3b.yaml --stage all | |
| # Verify GPU setup | |
| echo "" | |
| echo "=========================================" | |
| echo "Environment Verification" | |
| echo "=========================================" | |
| python3 -c " | |
| import torch | |
| print(f'PyTorch: {torch.__version__}') | |
| print(f'CUDA available: {torch.cuda.is_available()}') | |
| print(f'GPU count: {torch.cuda.device_count()}') | |
| for i in range(torch.cuda.device_count()): | |
| name = torch.cuda.get_device_name(i) | |
| mem = torch.cuda.get_device_properties(i).total_mem / 1e9 | |
| print(f' GPU {i}: {name} ({mem:.1f} GB)') | |
| print(f'NCCL available: {torch.distributed.is_nccl_available()}') | |
| " | |
| echo "" | |
| echo "=========================================" | |
| echo "Setup complete! Run training with:" | |
| echo " bash scripts/launch_training.sh" | |
| echo "=========================================" | |