rank1-7b
rank1-7b (jhu-clsp, 2025) is a 7 billion parameter chat model. rank1-7b is an open-weights chat model with roughly 7 billion parameters.
by jhu-clsp · 7B parameters
Best for
Ways to use rank1-7b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your jhu-clsp API key. osFoundry discovers rank1-7b 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
rank1-7b 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.
What hardware can run rank1-7b
rank1-7b runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
rank1-7b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about rank1-7b
Is rank1-7b free to use?
rank1-7b 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 rank1-7b commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does rank1-7b need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run rank1-7b locally?
Yes. rank1-7b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is rank1-7b best at?
rank1-7b is well-suited to text ranking.
How do I use rank1-7b in osFoundry?
Paste your jhu-clsp API key in the key dialog (or deploy the open weights for self-hostable models), assign rank1-7b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by jhu-clsp on February 18, 2025. Source: https://huggingface.co/jhu-clsp/rank1-7b