checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct is a 70 billion parameter chat model from adamkarvonen, released October 26, 2025. checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct is an open-weights chat model with roughly 70 billion parameters.
by adamkarvonen · 70B parameters
Best for
Ways to use checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your adamkarvonen API key. osFoundry discovers checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct 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
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct 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 checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct runs on a single A100 80GB or H100 80GB at Q4 quantisation (~42 GB VRAM with KV-cache headroom). Full-precision inference requires multiple H100/H200 GPUs at FP16 (~168 GB).
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct
Is checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct free to use?
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct 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 checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct 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 checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct need?
Approximately 42 GB at Q4 quantisation, or 168 GB at full FP16 precision. Fits on a single A100/H100 80GB.
Can I run checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct locally?
Yes. checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct best at?
checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct is well-suited to text generation.
How do I use checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct in osFoundry?
Paste your adamkarvonen API key in the key dialog (or deploy the open weights for self-hostable models), assign checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by adamkarvonen on October 26, 2025. Source: https://huggingface.co/adamkarvonen/checkpoints_act_cls_latentqa_pretrain_mix_adding_Llama-3_3-70B-Instruct