internlm2-math-plus-1_8b
internlm2-math-plus-1_8b is a 8 billion parameter chat model from internlm, released May 24, 2024. internlm2-math-plus-1_8b is an open-weights chat model with roughly 8 billion parameters.
by internlm · 8B parameters
Best for
Ways to use internlm2-math-plus-1_8b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your internlm API key. osFoundry discovers internlm2-math-plus-1_8b 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
internlm2-math-plus-1_8b 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 internlm2-math-plus-1_8b
internlm2-math-plus-1_8b 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).
internlm2-math-plus-1_8b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about internlm2-math-plus-1_8b
Is internlm2-math-plus-1_8b free to use?
internlm2-math-plus-1_8b 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 internlm2-math-plus-1_8b 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 internlm2-math-plus-1_8b need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run internlm2-math-plus-1_8b locally?
Yes. internlm2-math-plus-1_8b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is internlm2-math-plus-1_8b best at?
internlm2-math-plus-1_8b is well-suited to text generation.
How do I use internlm2-math-plus-1_8b in osFoundry?
Paste your internlm API key in the key dialog (or deploy the open weights for self-hostable models), assign internlm2-math-plus-1_8b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by internlm on May 24, 2024. Source: https://huggingface.co/internlm/internlm2-math-plus-1_8b