MedVLThinker-7B-RL_PMC
Released by UCSC-VLAA in 2025, MedVLThinker-7B-RL_PMC is a 7 billion parameter image-generation model. MedVLThinker-7B-RL_PMC is an open-weights image model with roughly 7 billion parameters.
by UCSC-VLAA · 7B parameters
Best for
Ways to use MedVLThinker-7B-RL_PMC in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your UCSC-VLAA API key. osFoundry discovers MedVLThinker-7B-RL_PMC 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
MedVLThinker-7B-RL_PMC 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 MedVLThinker-7B-RL_PMC
MedVLThinker-7B-RL_PMC runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
MedVLThinker-7B-RL_PMC vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about MedVLThinker-7B-RL_PMC
Is MedVLThinker-7B-RL_PMC free to use?
MedVLThinker-7B-RL_PMC 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 MedVLThinker-7B-RL_PMC 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 MedVLThinker-7B-RL_PMC need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run MedVLThinker-7B-RL_PMC locally?
Yes. MedVLThinker-7B-RL_PMC is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is MedVLThinker-7B-RL_PMC best at?
MedVLThinker-7B-RL_PMC is well-suited to image text to text.
How do I use MedVLThinker-7B-RL_PMC in osFoundry?
Paste your UCSC-VLAA API key in the key dialog (or deploy the open weights for self-hostable models), assign MedVLThinker-7B-RL_PMC to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by UCSC-VLAA on August 2, 2025. Source: https://huggingface.co/UCSC-VLAA/MedVLThinker-7B-RL_PMC