Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples
Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples (AgPerry, 2026) is a 14 billion parameter chat model. Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples is an open-weights chat model with roughly 14 billion parameters.
by AgPerry · 14B parameters
Best for
Ways to use Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your AgPerry API key. osFoundry discovers Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples
Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples runs on a single 16GB consumer GPU (~9 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~34 GB).
Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples
Is Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples free to use?
Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples need?
Approximately 9 GB at Q4 quantisation, or 34 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples locally?
Yes. Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples 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-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples best at?
Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples is well-suited to text generation.
How do I use Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples in osFoundry?
Paste your AgPerry API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by AgPerry on April 15, 2026. Source: https://huggingface.co/AgPerry/Qwen2.5-Coder-14B-Instruct-num11_v1-v2-v3-pairs-v3-triples