Qwen3-0.6B-4bit-DWQ
mlx-community's Qwen3-0.6B-4bit-DWQ packs 1 billion parameters into a chat model. Qwen3-0.6B-4bit-DWQ is an open-weights chat model with roughly 1 billion parameters.
by mlx-community · 1B parameters
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Ways to use Qwen3-0.6B-4bit-DWQ in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mlx-community API key. osFoundry discovers Qwen3-0.6B-4bit-DWQ 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
Qwen3-0.6B-4bit-DWQ 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 Qwen3-0.6B-4bit-DWQ
Qwen3-0.6B-4bit-DWQ 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).
Qwen3-0.6B-4bit-DWQ vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen3-0.6B-4bit-DWQ
Is Qwen3-0.6B-4bit-DWQ free to use?
Qwen3-0.6B-4bit-DWQ 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 Qwen3-0.6B-4bit-DWQ 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 Qwen3-0.6B-4bit-DWQ need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen3-0.6B-4bit-DWQ locally?
Yes. Qwen3-0.6B-4bit-DWQ is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen3-0.6B-4bit-DWQ best at?
Qwen3-0.6B-4bit-DWQ is well-suited to text generation.
How do I use Qwen3-0.6B-4bit-DWQ in osFoundry?
Paste your mlx-community API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen3-0.6B-4bit-DWQ to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mlx-community on May 3, 2025. Source: https://huggingface.co/mlx-community/Qwen3-0.6B-4bit-DWQ