acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math is a 8 billion parameter chat model from sstoica12, released April 14, 2026. acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math is an open-weights chat model with roughly 8 billion parameters.
by sstoica12 · 8B parameters
Best for
Ways to use acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your sstoica12 API key. osFoundry discovers acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math 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
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math 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 acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math 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).
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math
Is acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math free to use?
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math 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 acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math 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 acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math need?
Approximately 5 GB at Q4 quantisation, or 20 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math locally?
Yes. acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math best at?
acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math is well-suited to text generation.
How do I use acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math in osFoundry?
Paste your sstoica12 API key in the key dialog (or deploy the open weights for self-hostable models), assign acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by sstoica12 on April 14, 2026. Source: https://huggingface.co/sstoica12/acquisition_metamath_llama_instruct-3_1-8b-math_answer_variance_500_combined_openr1math