cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf
Released by RichardErkhov in 2024, cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf is a 60 billion parameter chat model. cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf is an open-weights chat model with roughly 60 billion parameters.
by RichardErkhov · 60B parameters
Best for
- complex multi-step reasoning
- agent orchestration with tool use
- long-document analysis and summarisation
Ways to use cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your RichardErkhov API key. osFoundry discovers cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf 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
cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf 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 cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf
cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf runs on a single A100 40GB at Q4 quantisation (~36 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~144 GB).
cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf
Is cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf free to use?
cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf 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 cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf 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 cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf need?
Approximately 36 GB at Q4 quantisation, or 144 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf locally?
Yes. cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf best at?
cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf is well-suited to complex multi-step reasoning, agent orchestration with tool use, long-document analysis and summarisation.
How do I use cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf in osFoundry?
Paste your RichardErkhov API key in the key dialog (or deploy the open weights for self-hostable models), assign cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by RichardErkhov on October 10, 2024. Source: https://huggingface.co/RichardErkhov/cloudyu_-_Yi-34Bx2-MoE-60B-DPO-gguf