LFM2.5-1.2B-Thinking-CodeX
Built by FlameF0X, LFM2.5-1.2B-Thinking-CodeX is a 1 billion parameter chat model. LFM2.5-1.2B-Thinking-CodeX is an open-weights chat model with roughly 1 billion parameters.
by FlameF0X · 1B parameters
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Ways to use LFM2.5-1.2B-Thinking-CodeX in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your FlameF0X API key. osFoundry discovers LFM2.5-1.2B-Thinking-CodeX 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-Thinking-CodeX 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-Thinking-CodeX
LFM2.5-1.2B-Thinking-CodeX 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-Thinking-CodeX 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-Thinking-CodeX
Is LFM2.5-1.2B-Thinking-CodeX free to use?
LFM2.5-1.2B-Thinking-CodeX 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-Thinking-CodeX 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-Thinking-CodeX 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-Thinking-CodeX locally?
Yes. LFM2.5-1.2B-Thinking-CodeX 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-Thinking-CodeX best at?
LFM2.5-1.2B-Thinking-CodeX is well-suited to text generation.
How do I use LFM2.5-1.2B-Thinking-CodeX in osFoundry?
Paste your FlameF0X API key in the key dialog (or deploy the open weights for self-hostable models), assign LFM2.5-1.2B-Thinking-CodeX to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by FlameF0X on April 26, 2026. Source: https://huggingface.co/FlameF0X/LFM2.5-1.2B-Thinking-CodeX