exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 is a 4 billion parameter chat model from alessiodevoto, released August 28, 2025. exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 is an open-weights chat model with roughly 4 billion parameters.
by alessiodevoto · 4B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your alessiodevoto API key. osFoundry discovers exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 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
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 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 exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 runs on a single 16GB consumer GPU (~3 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~10 GB).
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4
Is exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 free to use?
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 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 exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 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 exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 need?
Approximately 3 GB at Q4 quantisation, or 10 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 locally?
Yes. exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 best at?
exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 in osFoundry?
Paste your alessiodevoto API key in the key dialog (or deploy the open weights for self-hostable models), assign exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by alessiodevoto on August 28, 2025. Source: https://huggingface.co/alessiodevoto/exp_att_stats_Qwen_Qwen3-4B-Instruct-2507_kmfoda_booksum_100_1000_4