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