mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz is a 7 billion parameter chat model from Raghav-Singhal, released April 18, 2026. mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz is an open-weights chat model with roughly 7 billion parameters.
by Raghav-Singhal · 7B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Raghav-Singhal API key. osFoundry discovers mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz 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
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz 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 mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz
Is mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz free to use?
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz 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 mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz 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 mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz locally?
Yes. mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz best at?
mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz in osFoundry?
Paste your Raghav-Singhal API key in the key dialog (or deploy the open weights for self-hostable models), assign mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Raghav-Singhal on April 18, 2026. Source: https://huggingface.co/Raghav-Singhal/mixsft-normal-smollm-1p7b-500B-30n-2048sl-960gbsz