LLM2Vec-Meta-Llama-32-3B-Instruct-mntp
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp is a 3 billion parameter chat model from McGill-NLP, released November 15, 2025. LLM2Vec-Meta-Llama-32-3B-Instruct-mntp is an open-weights chat model with roughly 3 billion parameters.
by McGill-NLP · 3B parameters
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use LLM2Vec-Meta-Llama-32-3B-Instruct-mntp in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your McGill-NLP API key. osFoundry discovers LLM2Vec-Meta-Llama-32-3B-Instruct-mntp 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
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp 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 LLM2Vec-Meta-Llama-32-3B-Instruct-mntp
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp runs on a single 16GB consumer GPU (~2 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~8 GB).
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about LLM2Vec-Meta-Llama-32-3B-Instruct-mntp
Is LLM2Vec-Meta-Llama-32-3B-Instruct-mntp free to use?
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp 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 LLM2Vec-Meta-Llama-32-3B-Instruct-mntp 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 LLM2Vec-Meta-Llama-32-3B-Instruct-mntp need?
Approximately 2 GB at Q4 quantisation, or 8 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run LLM2Vec-Meta-Llama-32-3B-Instruct-mntp locally?
Yes. LLM2Vec-Meta-Llama-32-3B-Instruct-mntp is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is LLM2Vec-Meta-Llama-32-3B-Instruct-mntp best at?
LLM2Vec-Meta-Llama-32-3B-Instruct-mntp is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use LLM2Vec-Meta-Llama-32-3B-Instruct-mntp in osFoundry?
Paste your McGill-NLP API key in the key dialog (or deploy the open weights for self-hostable models), assign LLM2Vec-Meta-Llama-32-3B-Instruct-mntp to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by McGill-NLP on November 15, 2025. Source: https://huggingface.co/McGill-NLP/LLM2Vec-Meta-Llama-32-3B-Instruct-mntp