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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hardiksa/arcisvlm", device_map="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
File size: 2,133 Bytes
7a564e3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | """Push checkpoints to GitHub via Git LFS.
Called automatically at the end of training, or manually.
Requires GH_TOKEN environment variable.
"""
import os
import subprocess
import sys
def run(cmd, check=True):
"""Run a shell command and return output."""
result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
if check and result.returncode != 0:
print(f"Command failed: {cmd}")
print(f"stderr: {result.stderr}")
return None
return result.stdout.strip()
def main():
repo_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
os.chdir(repo_dir)
# Check for new checkpoints
checkpoints = []
ckpt_dir = os.path.join(repo_dir, "checkpoints")
if os.path.exists(ckpt_dir):
for f in os.listdir(ckpt_dir):
if f.endswith((".pt", ".pth", ".json", ".bin", ".safetensors")):
checkpoints.append(os.path.join("checkpoints", f))
if not checkpoints:
print("No checkpoints found to push.")
return
print(f"Found {len(checkpoints)} checkpoint files:")
for cp in checkpoints:
size = os.path.getsize(cp)
print(f" {cp}: {size / 1e9:.2f} GB" if size > 1e6 else f" {cp}: {size / 1e3:.1f} KB")
# Configure git
run("git config user.name 'Hardik Sanghvi'")
run("git config user.email 'hardik@adiance.com'")
# Stage checkpoint files
for cp in checkpoints:
run(f"git add -f {cp}")
# Also add any updated configs or results
run("git add -f configs/ || true")
run("git add -f checkpoints/benchmark_results.json || true")
# Commit
status = run("git status --porcelain")
if not status:
print("No changes to commit.")
return
run('git commit -m "feat: update checkpoints from training run"')
# Push
print("Pushing to GitHub (with LFS)...")
result = run("git push origin main 2>&1", check=False)
if result:
print(result)
print("Push successful!")
else:
print("Push failed. You may need to set GH_TOKEN or push manually.")
if __name__ == "__main__":
main()
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