OTel-Embedding-34M
Built by farbodtavakkoli, OTel-Embedding-34M is an embedding model. OTel-Embedding-34M is an open-weights embed model.
by farbodtavakkoli
Best for
Ways to use OTel-Embedding-34M in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your farbodtavakkoli API key. osFoundry discovers OTel-Embedding-34M 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
OTel-Embedding-34M 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.
OTel-Embedding-34M vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about OTel-Embedding-34M
Is OTel-Embedding-34M free to use?
OTel-Embedding-34M 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 OTel-Embedding-34M 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 OTel-Embedding-34M locally?
Yes. OTel-Embedding-34M is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is OTel-Embedding-34M best at?
OTel-Embedding-34M is well-suited to feature extraction.
How do I use OTel-Embedding-34M in osFoundry?
Paste your farbodtavakkoli API key in the key dialog (or deploy the open weights for self-hostable models), assign OTel-Embedding-34M to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by farbodtavakkoli on February 2, 2026. Source: https://huggingface.co/farbodtavakkoli/OTel-Embedding-34M