HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L
Released by wanxiren in 2025, HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L is a 8 billion parameter chat model. HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L is an open-weights chat model with roughly 8 billion parameters.
by wanxiren · 8B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your wanxiren API key. osFoundry discovers HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L 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
HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L 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 HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L
HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L 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).
HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L
Is HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L free to use?
HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L 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 HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L 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 HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L locally?
Yes. HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L best at?
HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L in osFoundry?
Paste your wanxiren API key in the key dialog (or deploy the open weights for self-hostable models), assign HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by wanxiren on February 21, 2025. Source: https://huggingface.co/wanxiren/HHO-DeepSeek-R1-Distill-Llama-8B-4bit-16L