d692685b-fdb4-4776-a4a8-c03c6c89a0f9
Built by mrHunghddddd, d692685b-fdb4-4776-a4a8-c03c6c89a0f9 is a 692685 billion parameter chat model. d692685b-fdb4-4776-a4a8-c03c6c89a0f9 is an open-weights chat model with roughly 692685 billion parameters.
by mrHunghddddd · 692685B parameters
Best for
- complex multi-step reasoning
- agent orchestration with tool use
- long-document analysis and summarisation
Ways to use d692685b-fdb4-4776-a4a8-c03c6c89a0f9 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your mrHunghddddd API key. osFoundry discovers d692685b-fdb4-4776-a4a8-c03c6c89a0f9 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
d692685b-fdb4-4776-a4a8-c03c6c89a0f9 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 d692685b-fdb4-4776-a4a8-c03c6c89a0f9
d692685b-fdb4-4776-a4a8-c03c6c89a0f9 runs on a multi-GPU setup or H200 141GB at Q4 (~415611 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~1662444 GB).
d692685b-fdb4-4776-a4a8-c03c6c89a0f9 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about d692685b-fdb4-4776-a4a8-c03c6c89a0f9
Is d692685b-fdb4-4776-a4a8-c03c6c89a0f9 free to use?
d692685b-fdb4-4776-a4a8-c03c6c89a0f9 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 d692685b-fdb4-4776-a4a8-c03c6c89a0f9 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 d692685b-fdb4-4776-a4a8-c03c6c89a0f9 need?
Approximately 415611 GB at Q4 quantisation, or 1662444 GB at full FP16 precision. Requires multi-GPU at higher quantisation.
Can I run d692685b-fdb4-4776-a4a8-c03c6c89a0f9 locally?
Yes. d692685b-fdb4-4776-a4a8-c03c6c89a0f9 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is d692685b-fdb4-4776-a4a8-c03c6c89a0f9 best at?
d692685b-fdb4-4776-a4a8-c03c6c89a0f9 is well-suited to complex multi-step reasoning, agent orchestration with tool use, long-document analysis and summarisation.
How do I use d692685b-fdb4-4776-a4a8-c03c6c89a0f9 in osFoundry?
Paste your mrHunghddddd API key in the key dialog (or deploy the open weights for self-hostable models), assign d692685b-fdb4-4776-a4a8-c03c6c89a0f9 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by mrHunghddddd on January 15, 2025. Source: https://huggingface.co/mrHunghddddd/d692685b-fdb4-4776-a4a8-c03c6c89a0f9