tomoro-colqwen3-embed-4b
tomoro-colqwen3-embed-4b (TomoroAI, 2025) is a 4 billion parameter chat model. tomoro-colqwen3-embed-4b is an open-weights chat model with roughly 4 billion parameters.
by TomoroAI · 4B parameters
Best for
- visual document retrieval
Ways to use tomoro-colqwen3-embed-4b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your TomoroAI API key. osFoundry discovers tomoro-colqwen3-embed-4b 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
tomoro-colqwen3-embed-4b 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 tomoro-colqwen3-embed-4b
tomoro-colqwen3-embed-4b 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).
tomoro-colqwen3-embed-4b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about tomoro-colqwen3-embed-4b
Is tomoro-colqwen3-embed-4b free to use?
tomoro-colqwen3-embed-4b 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 tomoro-colqwen3-embed-4b 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 tomoro-colqwen3-embed-4b need?
Approximately 3 GB at Q4 quantisation, or 10 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run tomoro-colqwen3-embed-4b locally?
Yes. tomoro-colqwen3-embed-4b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is tomoro-colqwen3-embed-4b best at?
tomoro-colqwen3-embed-4b is well-suited to visual document retrieval.
How do I use tomoro-colqwen3-embed-4b in osFoundry?
Paste your TomoroAI API key in the key dialog (or deploy the open weights for self-hostable models), assign tomoro-colqwen3-embed-4b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by TomoroAI on November 25, 2025. Source: https://huggingface.co/TomoroAI/tomoro-colqwen3-embed-4b