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Vet-Rate-org/Vet-Rate-Vision-Phi
Vet-Rate-Vision-Phi is a image-text-to-text model from Vet-Rate-org. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for mlc-llm. The card lists the license as mit.
A WebGPU-optimized compilation of Microsoft's Phi-3.5 Vision model for browser-based inference.
Downloads · 30 days
6
21% of all-time downloads
All-time downloads
29
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.bin2.8 GB · 100%
From the Hugging Face model README
A WebGPU-optimized compilation of Microsoft's Phi-3.5 Vision model for browser-based inference.
This is a quantized (q4f32_1) version of microsoft/Phi-3.5-vision-instruct compiled for WebGPU using MLC-LLM. It's designed for use with WebLLM in standard browsers without requiring experimental Chrome flags.
| Property | Value |
|---|---|
| Base Model | microsoft/Phi-3.5-vision-instruct |
| Quantization | q4f32_1 (int4, float32 model dtype) |
| Model Size | ~2.6 GB (quantized weights) |
| WASM Library | 6.6 MB |
| Context Window | 131,072 tokens |
| Parameters | ~4B |
| Vision Encoder | CLIP ViT-L/14 (336px) |
import { CreateMLCEngine } from "@mlc-ai/web-llm";
const engine = await CreateMLCEngine("Vet-Rate-org/Vet-Rate-Vision-Phi");
// Text-only chat
const response = await engine.chat.completions.create({
messages: [{ role: "user", content: "Hello!" }]
});
// Vision chat (with image)
const response = await engine.chat.completions.create({
messages: [{
role: "user",
content: [
{ type: "image_url", image_url: { url: "data:image/jpeg;base64,..." } },
{ type: "text", text: "What do you see in this image?" }
]
}]
});