qwen36-35b-a3b-ud-iq4xs-128k-github-copilot
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot is a 35 billion parameter chat model from johnml1135, released April 17, 2026. qwen36-35b-a3b-ud-iq4xs-128k-github-copilot is an open-weights chat model with roughly 35 billion parameters.
by johnml1135 · 35B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use qwen36-35b-a3b-ud-iq4xs-128k-github-copilot in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your johnml1135 API key. osFoundry discovers qwen36-35b-a3b-ud-iq4xs-128k-github-copilot 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
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot 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 qwen36-35b-a3b-ud-iq4xs-128k-github-copilot
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot runs on a 24GB consumer or workstation GPU (~21 GB VRAM with KV-cache headroom). Full-precision inference requires an H200 141GB or 2x A100 80GB at FP16 (~84 GB).
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about qwen36-35b-a3b-ud-iq4xs-128k-github-copilot
Is qwen36-35b-a3b-ud-iq4xs-128k-github-copilot free to use?
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot 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 qwen36-35b-a3b-ud-iq4xs-128k-github-copilot 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 qwen36-35b-a3b-ud-iq4xs-128k-github-copilot need?
Approximately 21 GB at Q4 quantisation, or 84 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run qwen36-35b-a3b-ud-iq4xs-128k-github-copilot locally?
Yes. qwen36-35b-a3b-ud-iq4xs-128k-github-copilot is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is qwen36-35b-a3b-ud-iq4xs-128k-github-copilot best at?
qwen36-35b-a3b-ud-iq4xs-128k-github-copilot is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use qwen36-35b-a3b-ud-iq4xs-128k-github-copilot in osFoundry?
Paste your johnml1135 API key in the key dialog (or deploy the open weights for self-hostable models), assign qwen36-35b-a3b-ud-iq4xs-128k-github-copilot to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by johnml1135 on April 17, 2026. Source: https://huggingface.co/johnml1135/qwen36-35b-a3b-ud-iq4xs-128k-github-copilot