Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus
ypszn's Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus packs 1 billion parameters into a chat model. Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus is an open-weights chat model with roughly 1 billion parameters.
by ypszn · 1B parameters
Best for
Ways to use Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your ypszn API key. osFoundry discovers Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus 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
Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus 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 Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus
Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus 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).
Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus
Is Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus free to use?
Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus 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 Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus 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 Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus locally?
Yes. Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus best at?
Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus is well-suited to text generation.
How do I use Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus in osFoundry?
Paste your ypszn API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by ypszn on November 23, 2025. Source: https://huggingface.co/ypszn/Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-dormant_omnivorous_walrus