Gemma3-1b-SFT_Teached
Built by SAIJO1233, Gemma3-1b-SFT_Teached is a 1 billion parameter chat model. Gemma3-1b-SFT_Teached is an open-weights chat model with roughly 1 billion parameters.
by SAIJO1233 · 1B parameters
Best for
Ways to use Gemma3-1b-SFT_Teached in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your SAIJO1233 API key. osFoundry discovers Gemma3-1b-SFT_Teached 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
Gemma3-1b-SFT_Teached 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 Gemma3-1b-SFT_Teached
Gemma3-1b-SFT_Teached runs on a single 16GB consumer GPU (~1 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~3 GB).
Gemma3-1b-SFT_Teached vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Gemma3-1b-SFT_Teached
Is Gemma3-1b-SFT_Teached free to use?
Gemma3-1b-SFT_Teached 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 Gemma3-1b-SFT_Teached 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 Gemma3-1b-SFT_Teached need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Gemma3-1b-SFT_Teached locally?
Yes. Gemma3-1b-SFT_Teached is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Gemma3-1b-SFT_Teached best at?
Gemma3-1b-SFT_Teached is well-suited to text generation.
How do I use Gemma3-1b-SFT_Teached in osFoundry?
Paste your SAIJO1233 API key in the key dialog (or deploy the open weights for self-hostable models), assign Gemma3-1b-SFT_Teached to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by SAIJO1233 on April 23, 2026. Source: https://huggingface.co/SAIJO1233/Gemma3-1b-SFT_Teached