EmergentRP-Qwen4B
benhs000's EmergentRP-Qwen4B packs 4 billion parameters into a chat model. EmergentRP-Qwen4B is an open-weights chat model with roughly 4 billion parameters.
by benhs000 · 4B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use EmergentRP-Qwen4B in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your benhs000 API key. osFoundry discovers EmergentRP-Qwen4B 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
EmergentRP-Qwen4B 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 EmergentRP-Qwen4B
EmergentRP-Qwen4B runs on a single 16GB consumer GPU (~3 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~10 GB).
EmergentRP-Qwen4B vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about EmergentRP-Qwen4B
Is EmergentRP-Qwen4B free to use?
EmergentRP-Qwen4B 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 EmergentRP-Qwen4B 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 EmergentRP-Qwen4B need?
Approximately 3 GB at Q4 quantisation, or 10 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run EmergentRP-Qwen4B locally?
Yes. EmergentRP-Qwen4B is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is EmergentRP-Qwen4B best at?
EmergentRP-Qwen4B is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use EmergentRP-Qwen4B in osFoundry?
Paste your benhs000 API key in the key dialog (or deploy the open weights for self-hostable models), assign EmergentRP-Qwen4B to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by benhs000 on October 13, 2025. Source: https://huggingface.co/benhs000/EmergentRP-Qwen4B