MCW_KD_Teacher_Qwen1.5-1.8B
MCW_KD_Teacher_Qwen1.5-1.8B (HoangTran223, 2025) is a 2 billion parameter chat model. MCW_KD_Teacher_Qwen1.5-1.8B is an open-weights chat model with roughly 2 billion parameters.
by HoangTran223 · 2B parameters
Best for
Ways to use MCW_KD_Teacher_Qwen1.5-1.8B in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your HoangTran223 API key. osFoundry discovers MCW_KD_Teacher_Qwen1.5-1.8B 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
MCW_KD_Teacher_Qwen1.5-1.8B 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 MCW_KD_Teacher_Qwen1.5-1.8B
MCW_KD_Teacher_Qwen1.5-1.8B runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~5 GB).
MCW_KD_Teacher_Qwen1.5-1.8B vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about MCW_KD_Teacher_Qwen1.5-1.8B
Is MCW_KD_Teacher_Qwen1.5-1.8B free to use?
MCW_KD_Teacher_Qwen1.5-1.8B 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 MCW_KD_Teacher_Qwen1.5-1.8B 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 MCW_KD_Teacher_Qwen1.5-1.8B need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run MCW_KD_Teacher_Qwen1.5-1.8B locally?
Yes. MCW_KD_Teacher_Qwen1.5-1.8B is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is MCW_KD_Teacher_Qwen1.5-1.8B best at?
MCW_KD_Teacher_Qwen1.5-1.8B is well-suited to text generation.
How do I use MCW_KD_Teacher_Qwen1.5-1.8B in osFoundry?
Paste your HoangTran223 API key in the key dialog (or deploy the open weights for self-hostable models), assign MCW_KD_Teacher_Qwen1.5-1.8B to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by HoangTran223 on July 12, 2025. Source: https://huggingface.co/HoangTran223/MCW_KD_Teacher_Qwen1.5-1.8B