GeM2-Llamion-14B-Chat
GeM2-Llamion-14B-Chat (vaiv, 2024) is a 14 billion parameter chat model. GeM2-Llamion-14B-Chat is an open-weights chat model with roughly 14 billion parameters.
by vaiv · 14B parameters
Best for
Ways to use GeM2-Llamion-14B-Chat in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your vaiv API key. osFoundry discovers GeM2-Llamion-14B-Chat 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
GeM2-Llamion-14B-Chat 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 GeM2-Llamion-14B-Chat
GeM2-Llamion-14B-Chat runs on a single 16GB consumer GPU (~9 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~34 GB).
GeM2-Llamion-14B-Chat vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about GeM2-Llamion-14B-Chat
Is GeM2-Llamion-14B-Chat free to use?
GeM2-Llamion-14B-Chat 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 GeM2-Llamion-14B-Chat 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 GeM2-Llamion-14B-Chat need?
Approximately 9 GB at Q4 quantisation, or 34 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run GeM2-Llamion-14B-Chat locally?
Yes. GeM2-Llamion-14B-Chat is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is GeM2-Llamion-14B-Chat best at?
GeM2-Llamion-14B-Chat is well-suited to text generation.
How do I use GeM2-Llamion-14B-Chat in osFoundry?
Paste your vaiv API key in the key dialog (or deploy the open weights for self-hostable models), assign GeM2-Llamion-14B-Chat to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by vaiv on May 13, 2024. Source: https://huggingface.co/vaiv/GeM2-Llamion-14B-Chat