tf_efficientnet_l2.ns_jft_in1k_475
tf_efficientnet_l2.ns_jft_in1k_475 is a image-generation model from timm, released December 13, 2022. tf_efficientnet_l2.ns_jft_in1k_475 is an open-weights image model.
by timm
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Ways to use tf_efficientnet_l2.ns_jft_in1k_475 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your timm API key. osFoundry discovers tf_efficientnet_l2.ns_jft_in1k_475 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
tf_efficientnet_l2.ns_jft_in1k_475 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.
tf_efficientnet_l2.ns_jft_in1k_475 vs similar models
| Model | Org | Params | Context | Input price | Self-host |
|---|
| tf_efficientnet_l2.ns_jft_in1k_475 | timm | — | — | Free (local) | Yes |
| Wan2_Undressing_-_V1 | highscoregames12018 | — | — | Free (local) | Yes |
| RoentGen-v2 | stanfordmimi | — | — | Free (local) | Yes |
| TR_OCR_LARGE | David-Magdy | — | — | Free (local) | Yes |
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about tf_efficientnet_l2.ns_jft_in1k_475
Is tf_efficientnet_l2.ns_jft_in1k_475 free to use?
tf_efficientnet_l2.ns_jft_in1k_475 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 tf_efficientnet_l2.ns_jft_in1k_475 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 tf_efficientnet_l2.ns_jft_in1k_475 locally?
Yes. tf_efficientnet_l2.ns_jft_in1k_475 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is tf_efficientnet_l2.ns_jft_in1k_475 best at?
tf_efficientnet_l2.ns_jft_in1k_475 is well-suited to image classification.
How do I use tf_efficientnet_l2.ns_jft_in1k_475 in osFoundry?
Paste your timm API key in the key dialog (or deploy the open weights for self-hostable models), assign tf_efficientnet_l2.ns_jft_in1k_475 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 December 13, 2022. Source: https://huggingface.co/timm/tf_efficientnet_l2.ns_jft_in1k_475