vit_xsmall_patch16_clip_224.tinyclip_yfcc15m
Released by timm in 2024, vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is an image-generation model. vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is an open-weights image model.
by timm
Best for
- zero shot image classification
Ways to use vit_xsmall_patch16_clip_224.tinyclip_yfcc15m in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your timm API key. osFoundry discovers vit_xsmall_patch16_clip_224.tinyclip_yfcc15m automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
vit_xsmall_patch16_clip_224.tinyclip_yfcc15m vs similar models
| Model | Org | Params | Context | Input price | Self-host |
|---|
| vit_xsmall_patch16_clip_224.tinyclip_yfcc15m | timm | — | — | Free (local) | Yes |
| LTX-2.3-fp8 | qqceqqq | — | — | Free (local) | Yes |
| Emma | Emma23123 | — | — | Free (local) | Yes |
| xl4supir | prolapse | — | — | Free (local) | Yes |
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about vit_xsmall_patch16_clip_224.tinyclip_yfcc15m
Is vit_xsmall_patch16_clip_224.tinyclip_yfcc15m free to use?
vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use vit_xsmall_patch16_clip_224.tinyclip_yfcc15m commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
Can I run vit_xsmall_patch16_clip_224.tinyclip_yfcc15m locally?
Yes. vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is vit_xsmall_patch16_clip_224.tinyclip_yfcc15m best at?
vit_xsmall_patch16_clip_224.tinyclip_yfcc15m is well-suited to zero shot image classification.
How do I use vit_xsmall_patch16_clip_224.tinyclip_yfcc15m in osFoundry?
Paste your timm API key in the key dialog (or deploy the open weights for self-hostable models), assign vit_xsmall_patch16_clip_224.tinyclip_yfcc15m to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by timm on March 20, 2024. Source: https://huggingface.co/timm/vit_xsmall_patch16_clip_224.tinyclip_yfcc15m