train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full is a 2 billion parameter chat model from Bisher, released May 30, 2025. train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full is an open-weights chat model with roughly 2 billion parameters.
by Bisher · 2B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Bisher API key. osFoundry discovers train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full 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
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full 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 train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~5 GB).
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full
Is train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full free to use?
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full 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 train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full 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 train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full need?
Approximately 2 GB at Q4 quantisation, or 5 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full locally?
Yes. train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full best at?
train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full in osFoundry?
Paste your Bisher API key in the key dialog (or deploy the open weights for self-hostable models), assign train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Bisher on May 30, 2025. Source: https://huggingface.co/Bisher/train_run-qwen2.5-1.5b-instruct-arabic-diacritization-full-fadel-L40S_full