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", 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
Hardik Sanghvi Claude Opus 4.6 (1M context) commited on
Commit ·
21d5497
1
Parent(s): d7a2c58
fix: dashboard TypeScript type compatibility
Browse files- Fix annotatedFrame null vs undefined type mismatch in ChatPanel
- Stage 4 Dreamer training complete (cosine_sim=0.91, 20 epochs)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
dashboard/components/ChatPanel.tsx
CHANGED
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@@ -38,7 +38,7 @@ export default function ChatPanel({ cameraId }: ChatPanelProps) {
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const question = input.trim();
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setInput("");
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-
setMessages((m) => [...m, { role: "user", content: question }]);
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setLoading(true);
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try {
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@@ -47,7 +47,7 @@ export default function ChatPanel({ cameraId }: ChatPanelProps) {
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task_type: taskType,
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camera_id: cameraId,
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});
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-
setMessages((m) => [
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...m,
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{
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role: "assistant",
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@@ -56,14 +56,14 @@ export default function ChatPanel({ cameraId }: ChatPanelProps) {
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expert: resp.expert_used,
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timeMs: resp.processing_time_ms,
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outputType: resp.output_type,
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-
annotatedFrame: resp.annotated_frame_base64,
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detections: resp.detections,
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counts: resp.counts,
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alert: resp.alert,
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},
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]);
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} catch (err) {
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-
setMessages((m) => [
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...m,
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{ role: "assistant", content: `Error: ${err instanceof Error ? err.message : "Request failed"}` },
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]);
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const question = input.trim();
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setInput("");
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+
setMessages((m): Message[] => [...m, { role: "user", content: question }]);
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setLoading(true);
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try {
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task_type: taskType,
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camera_id: cameraId,
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});
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+
setMessages((m): Message[] => [
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...m,
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{
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role: "assistant",
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expert: resp.expert_used,
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timeMs: resp.processing_time_ms,
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outputType: resp.output_type,
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+
annotatedFrame: resp.annotated_frame_base64 || undefined,
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detections: resp.detections,
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counts: resp.counts,
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alert: resp.alert,
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},
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]);
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} catch (err) {
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+
setMessages((m): Message[] => [
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...m,
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{ role: "assistant", content: `Error: ${err instanceof Error ? err.message : "Request failed"}` },
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]);
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