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
| version: "3.8" | |
| services: | |
| api: | |
| build: | |
| context: .. | |
| dockerfile: deploy/Dockerfile.api | |
| volumes: | |
| - checkpoints:/app/checkpoints | |
| environment: | |
| - ARCISVLM_CONFIG=configs/scale_1.3b.yaml | |
| - ARCISVLM_CHECKPOINT=${ARCISVLM_CHECKPOINT:-checkpoints/v3_stage1_final.pt} | |
| - ARCISVLM_DEVICE=cuda | |
| - HF_TOKEN=${HF_TOKEN:-} | |
| deploy: | |
| resources: | |
| reservations: | |
| devices: | |
| - driver: nvidia | |
| count: 1 | |
| capabilities: [gpu] | |
| restart: unless-stopped | |
| healthcheck: | |
| test: ["CMD", "curl", "-f", "http://localhost:8000/health"] | |
| interval: 30s | |
| timeout: 10s | |
| retries: 3 | |
| dashboard: | |
| build: | |
| context: .. | |
| dockerfile: deploy/Dockerfile.dashboard | |
| environment: | |
| - NEXT_PUBLIC_API_URL= | |
| - NEXT_PUBLIC_WS_URL= | |
| depends_on: | |
| - api | |
| restart: unless-stopped | |
| nginx: | |
| image: nginx:alpine | |
| ports: | |
| - "80:80" | |
| volumes: | |
| - ./nginx.conf:/etc/nginx/conf.d/default.conf:ro | |
| depends_on: | |
| - api | |
| - dashboard | |
| restart: unless-stopped | |
| volumes: | |
| checkpoints: | |