QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM
g4me's QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM packs 2 billion parameters into a chat model. QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM is an open-weights chat model with roughly 2 billion parameters.
by g4me · 2B parameters
Best for
Ways to use QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your g4me API key. osFoundry discovers QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM 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
QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM 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 QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM
QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~5 GB).
QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM
Is QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM free to use?
QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM 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 QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM 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 QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM locally?
Yes. QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM best at?
QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM is well-suited to text generation.
How do I use QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM in osFoundry?
Paste your g4me API key in the key dialog (or deploy the open weights for self-hostable models), assign QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by g4me on April 21, 2026. Source: https://huggingface.co/g4me/QwenRolina3-1.7B-base-LR1e5-b32g2gc8-AR-IRM