Llamole-Llama-3.1-8B-Instruct-Adapter
liuganghuggingface's Llamole-Llama-3.1-8B-Instruct-Adapter packs 8 billion parameters into a chat model. Llamole-Llama-3.1-8B-Instruct-Adapter is an open-weights chat model with roughly 8 billion parameters.
by liuganghuggingface · 8B parameters
Best for
Ways to use Llamole-Llama-3.1-8B-Instruct-Adapter in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your liuganghuggingface API key. osFoundry discovers Llamole-Llama-3.1-8B-Instruct-Adapter 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
Llamole-Llama-3.1-8B-Instruct-Adapter 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 Llamole-Llama-3.1-8B-Instruct-Adapter
Llamole-Llama-3.1-8B-Instruct-Adapter 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).
Llamole-Llama-3.1-8B-Instruct-Adapter vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Llamole-Llama-3.1-8B-Instruct-Adapter
Is Llamole-Llama-3.1-8B-Instruct-Adapter free to use?
Llamole-Llama-3.1-8B-Instruct-Adapter 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 Llamole-Llama-3.1-8B-Instruct-Adapter 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 Llamole-Llama-3.1-8B-Instruct-Adapter need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Llamole-Llama-3.1-8B-Instruct-Adapter locally?
Yes. Llamole-Llama-3.1-8B-Instruct-Adapter is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Llamole-Llama-3.1-8B-Instruct-Adapter best at?
Llamole-Llama-3.1-8B-Instruct-Adapter is well-suited to graph ml.
How do I use Llamole-Llama-3.1-8B-Instruct-Adapter in osFoundry?
Paste your liuganghuggingface API key in the key dialog (or deploy the open weights for self-hostable models), assign Llamole-Llama-3.1-8B-Instruct-Adapter to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by liuganghuggingface on October 7, 2024. Source: https://huggingface.co/liuganghuggingface/Llamole-Llama-3.1-8B-Instruct-Adapter