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
| # Single-GPU training to avoid NCCL inter-GPU communication issues on vast.ai | |
| # Uses 1x H100 with gradient accumulation to match effective batch size | |
| set -ex | |
| cd /root/arcisvlm | |
| export HF_TOKEN=$HF_TOKEN | |
| export CUDA_VISIBLE_DEVICES=0 | |
| # Clean corrupted checkpoints | |
| rm -f checkpoints/stage2_epoch*.pt checkpoints/stage2_final.pt checkpoints/stage3_*.pt | |
| echo "=== Stage 2: Single GPU Training ===" | |
| date | |
| nvidia-smi --query-gpu=name,memory.total --format=csv,noheader | |
| # Single GPU — no torchrun, no DDP | |
| # Use nproc=1 so DDP is trivial (single process group) | |
| torchrun --nproc_per_node=1 --master_port=29503 \ | |
| scripts/train_stage2_ddp.py \ | |
| --config configs/scale_1.3b.yaml \ | |
| --stage1_ckpt checkpoints/v3_stage1_final.pt | |
| STAGE2_EXIT=$? | |
| echo "Stage 2 exit code: $STAGE2_EXIT" | |
| date | |
| if [ $STAGE2_EXIT -ne 0 ]; then | |
| echo "!!! Stage 2 FAILED with exit code $STAGE2_EXIT !!!" | |
| echo "Checking for partial checkpoints..." | |
| ls -lh checkpoints/stage2_*.pt 2>/dev/null || echo "No stage2 checkpoints found" | |
| exit 1 | |
| fi | |
| echo "=== Pushing Stage 2 to HF ===" | |
| python3 scripts/push_to_hf.py stage2_final.pt v4_stage2_final.pt | |
| echo "=== Stage 3: Single GPU Training ===" | |
| date | |
| torchrun --nproc_per_node=1 --master_port=29503 \ | |
| scripts/train_stage3_ddp.py \ | |
| --config configs/scale_1.3b.yaml \ | |
| --stage2_ckpt checkpoints/stage2_final.pt | |
| STAGE3_EXIT=$? | |
| echo "Stage 3 exit code: $STAGE3_EXIT" | |
| date | |
| if [ $STAGE3_EXIT -ne 0 ]; then | |
| echo "!!! Stage 3 FAILED with exit code $STAGE3_EXIT !!!" | |
| ls -lh checkpoints/stage3_*.pt 2>/dev/null || echo "No stage3 checkpoints found" | |
| exit 1 | |
| fi | |
| echo "=== Pushing Stage 3 to HF ===" | |
| python3 scripts/push_to_hf.py stage3_final.pt v4_stage3_final.pt | |
| echo "=== ALL TRAINING COMPLETE ===" | |
| date | |
| echo "Checkpoints:" | |
| ls -lh checkpoints/ | |