gemma_4E4B-it_finetune
gemma_4E4B-it_finetune is a 4 billion parameter image-generation model from Capitaller, released April 19, 2026. gemma_4E4B-it_finetune is an open-weights image model with roughly 4 billion parameters.
by Capitaller · 4B parameters
Best for
Ways to use gemma_4E4B-it_finetune in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Capitaller API key. osFoundry discovers gemma_4E4B-it_finetune 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
gemma_4E4B-it_finetune 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 gemma_4E4B-it_finetune
gemma_4E4B-it_finetune 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).
gemma_4E4B-it_finetune vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about gemma_4E4B-it_finetune
Is gemma_4E4B-it_finetune free to use?
gemma_4E4B-it_finetune 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 gemma_4E4B-it_finetune 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 gemma_4E4B-it_finetune need?
Approximately 3 GB at Q4 quantisation, or 10 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run gemma_4E4B-it_finetune locally?
Yes. gemma_4E4B-it_finetune is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is gemma_4E4B-it_finetune best at?
gemma_4E4B-it_finetune is well-suited to image text to text.
How do I use gemma_4E4B-it_finetune in osFoundry?
Paste your Capitaller API key in the key dialog (or deploy the open weights for self-hostable models), assign gemma_4E4B-it_finetune to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Capitaller on April 19, 2026. Source: https://huggingface.co/Capitaller/gemma_4E4B-it_finetune