NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse (fdschmidt93, 2024) is a 8 billion parameter embedding model. NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse is an open-weights embed model with roughly 8 billion parameters.
by fdschmidt93 · 8B parameters
Best for
Ways to use NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your fdschmidt93 API key. osFoundry discovers NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse 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
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse 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 NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse 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).
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse
Is NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse free to use?
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse 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 NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse 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 NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse locally?
Yes. NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse best at?
NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse is well-suited to sentence similarity.
How do I use NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse in osFoundry?
Paste your fdschmidt93 API key in the key dialog (or deploy the open weights for self-hostable models), assign NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by fdschmidt93 on October 1, 2024. Source: https://huggingface.co/fdschmidt93/NLLB-LLM2Vec-Meta-Llama-31-8B-Instruct-mntp-unsup-simcse