ram
Built by mterris, ram is an chat model. ram is an open-weights chat model.
by mterris
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use ram in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mterris API key. osFoundry discovers ram 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
ram 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.
ram vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about ram
Is ram free to use?
ram 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 ram 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 ram locally?
Yes. ram is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is ram best at?
ram is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use ram in osFoundry?
Paste your mterris API key in the key dialog (or deploy the open weights for self-hostable models), assign ram to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mterris on April 11, 2025. Source: https://huggingface.co/mterris/ram