LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM is a 1 billion parameter chat model from DavidAU, released February 6, 2026. LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM is an open-weights chat model with roughly 1 billion parameters.
by DavidAU · 1B parameters
Best for
Ways to use LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your DavidAU API key. osFoundry discovers LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM 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
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM 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 LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM runs on a single 16GB consumer GPU (~1 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~3 GB).
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM
Is LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM free to use?
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM 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 LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM 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 LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM locally?
Yes. LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM best at?
LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM is well-suited to text generation.
How do I use LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM in osFoundry?
Paste your DavidAU API key in the key dialog (or deploy the open weights for self-hostable models), assign LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by DavidAU on February 6, 2026. Source: https://huggingface.co/DavidAU/LFM2.5-1.2B-MEGABRAIN-Thinking-Claude-Polaris-Deepseek-GLM