unsup-Llama-3.2-1B-Instruct-only_mask_w_item
unsup-Llama-3.2-1B-Instruct-only_mask_w_item is a 1 billion parameter chat model from ferrazzipietro, released April 20, 2026. unsup-Llama-3.2-1B-Instruct-only_mask_w_item 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 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 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 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
unsup-Llama-3.2-1B-Instruct-only_mask_w_item 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 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
Is unsup-Llama-3.2-1B-Instruct-only_mask_w_item free to use?
unsup-Llama-3.2-1B-Instruct-only_mask_w_item 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 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 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 locally?
Yes. unsup-Llama-3.2-1B-Instruct-only_mask_w_item 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 best at?
unsup-Llama-3.2-1B-Instruct-only_mask_w_item 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 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 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 April 20, 2026. Source: https://huggingface.co/ferrazzipietro/unsup-Llama-3.2-1B-Instruct-only_mask_w_item