unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh is a 1 billion parameter chat model from ferrazzipietro, released May 4, 2026. unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh is an open-weights chat model with roughly 1 billion parameters.
by ferrazzipietro · 1B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your ferrazzipietro API key. osFoundry discovers unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh 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
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh 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 unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh 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).
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh
Is unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh free to use?
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh 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 unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh 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 unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh locally?
Yes. unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh best at?
unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh in osFoundry?
Paste your ferrazzipietro API key in the key dialog (or deploy the open weights for self-hostable models), assign unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by ferrazzipietro on May 4, 2026. Source: https://huggingface.co/ferrazzipietro/unsup-Llama-3.2-1B-Instruct-only_mask_w_item_mesh