math_pwd_lock_deepseek_math7b_on_weak_pythia1b
redwoodresearch's math_pwd_lock_deepseek_math7b_on_weak_pythia1b packs 1 billion parameters into a chat model. math_pwd_lock_deepseek_math7b_on_weak_pythia1b is an open-weights chat model with roughly 1 billion parameters.
by redwoodresearch · 1B parameters
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Ways to use math_pwd_lock_deepseek_math7b_on_weak_pythia1b in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your redwoodresearch API key. osFoundry discovers math_pwd_lock_deepseek_math7b_on_weak_pythia1b 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
math_pwd_lock_deepseek_math7b_on_weak_pythia1b 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 math_pwd_lock_deepseek_math7b_on_weak_pythia1b
math_pwd_lock_deepseek_math7b_on_weak_pythia1b 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).
math_pwd_lock_deepseek_math7b_on_weak_pythia1b vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about math_pwd_lock_deepseek_math7b_on_weak_pythia1b
Is math_pwd_lock_deepseek_math7b_on_weak_pythia1b free to use?
math_pwd_lock_deepseek_math7b_on_weak_pythia1b 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 math_pwd_lock_deepseek_math7b_on_weak_pythia1b 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 math_pwd_lock_deepseek_math7b_on_weak_pythia1b need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run math_pwd_lock_deepseek_math7b_on_weak_pythia1b locally?
Yes. math_pwd_lock_deepseek_math7b_on_weak_pythia1b is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is math_pwd_lock_deepseek_math7b_on_weak_pythia1b best at?
math_pwd_lock_deepseek_math7b_on_weak_pythia1b is well-suited to text generation.
How do I use math_pwd_lock_deepseek_math7b_on_weak_pythia1b in osFoundry?
Paste your redwoodresearch API key in the key dialog (or deploy the open weights for self-hostable models), assign math_pwd_lock_deepseek_math7b_on_weak_pythia1b to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by redwoodresearch on June 6, 2024. Source: https://huggingface.co/redwoodresearch/math_pwd_lock_deepseek_math7b_on_weak_pythia1b