qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128
Released by jackf857 in 2026, qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 is a 8 billion parameter chat model. qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 is an open-weights chat model with roughly 8 billion parameters.
by jackf857 · 8B parameters
Best for
Ways to use qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your jackf857 API key. osFoundry discovers qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 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
qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 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 qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128
qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 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 (~20 GB).
qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128
Is qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 free to use?
qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 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 qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 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 qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 locally?
Yes. qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 best at?
qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 is well-suited to text generation.
How do I use qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 in osFoundry?
Paste your jackf857 API key in the key dialog (or deploy the open weights for self-hostable models), assign qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by jackf857 on April 22, 2026. Source: https://huggingface.co/jackf857/qwen3-8b-base-epsilon-dpo-ultrafeedback-4xh200-batch-128