Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit
Released by mlx-community in 2024, Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit is a 14 billion parameter chat model. Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit is an open-weights chat model with roughly 14 billion parameters.
by mlx-community · 14B parameters
Best for
Ways to use Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mlx-community API key. osFoundry discovers Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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
Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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 Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit
Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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).
Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit
Is Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit free to use?
Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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 Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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 Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit need?
Approximately 9 GB at Q4 quantisation, or 34 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit locally?
Yes. Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit best at?
Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit is well-suited to text generation.
How do I use Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit in osFoundry?
Paste your mlx-community API key in the key dialog (or deploy the open weights for self-hostable models), assign Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit 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 December 9, 2024. Source: https://huggingface.co/mlx-community/Josiefied-Qwen2.5-14B-Instruct-abliterated-v4-8-bit