Qwen2.5-Coder-14B-Instruct-abliterated
MSJXMG's Qwen2.5-Coder-14B-Instruct-abliterated packs 14 billion parameters into a chat model. Qwen2.5-Coder-14B-Instruct-abliterated is an open-weights chat model with roughly 14 billion parameters.
by MSJXMG · 14B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use Qwen2.5-Coder-14B-Instruct-abliterated in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your MSJXMG API key. osFoundry discovers Qwen2.5-Coder-14B-Instruct-abliterated 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-abliterated 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-abliterated
Qwen2.5-Coder-14B-Instruct-abliterated 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-abliterated 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-abliterated
Is Qwen2.5-Coder-14B-Instruct-abliterated free to use?
Qwen2.5-Coder-14B-Instruct-abliterated 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-abliterated 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-abliterated 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-abliterated locally?
Yes. Qwen2.5-Coder-14B-Instruct-abliterated 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-abliterated best at?
Qwen2.5-Coder-14B-Instruct-abliterated is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use Qwen2.5-Coder-14B-Instruct-abliterated in osFoundry?
Paste your MSJXMG API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen2.5-Coder-14B-Instruct-abliterated to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by MSJXMG on April 15, 2026. Source: https://huggingface.co/MSJXMG/Qwen2.5-Coder-14B-Instruct-abliterated