Open Model Vault / archival blueprint

Back up the open-model world.

A no-conservative Windows plan for revision-pinned, resumable copies of frontier LLMs, multimodal systems, image/video generators, speech, TTS, music, retrieval, vision, and complete trusted-lab inventories.

June 12, 2026·Prepared by Hermes
Bottom line

Secure the audited 77-repository launch set (13.67 TiB), then run the official-organization crawler in all-formats mode. That second pass is the literal “everything” option: every public model repo from 53 trusted organizations, including old releases and duplicate deployment formats. Inventory first, provision the measured storage plus 15%, then let the resumable downloader run.

Audited launch set
77
immutable revisions
Launch storage
13.67 TiB
complete repository trees
Manual gates
13
terms or approval
Official orgs
53
dynamic mirror scope

Strategy: three concentric archives

Ring 1 — audited launch set

77 current high-value canonical checkpoints. Every source is first-party, every revision is pinned to a full commit SHA, and exact bytes came from Hugging Face’s recursive tree API.

Ring 2 — canonical official-org mirror

Every public model repository in trusted lab organizations, excluding only names that clearly signal derived GGUF/GPTQ/AWQ/MLX/ONNX/precision repacks. This catches every generation, base/instruct/reasoning sibling, size tier, embedding, reranker, OCR, safety, segmentation, audio, and research model.

Ring 3 — literal everything

Add --all-formats. This includes every official quantization and conversion too. It may require tens to hundreds of TiB; the crawler measures the exact live total before transfer.

Windows machine and storage design

Filesystem

Use a short root like D:\AI on NTFS or ReFS. Avoid FAT/exFAT. Enable Windows long paths.

Capacity

Launch set: 13.67 TiB. Everything mode: inventory first, then provision measured total + 15%. Keep at least 500 GiB free.

Network

Use wired Ethernet, disable sleep, and expect multi-day or multi-week transfers. Xet high-performance mode resumes chunks.

Redundancy

RAID is availability, not backup. Preserve manifests and gated snapshots on a second site.

  • Launch set: 20 TiB usable is comfortable.
  • Canonical org mirror: provision only after live inventory.
  • All formats: use an expandable storage server/JBOD, not a fixed workstation disk.

Windows runbook

Download the four attached files from the Discord reply into C:\ModelArchiveTools: Setup-OpenModelArchive.ps1, archive_windows.py, discover_official_orgs.py, and manifest_windows.json.

Setup and launch set

# Put the attached files in C:\ModelArchiveTools
Set-ExecutionPolicy -Scope Process Bypass
cd C:\ModelArchiveTools
.\Setup-OpenModelArchive.ps1

# Inspect, download, and resume the audited 77-repository set
python .\archive_windows.py --manifest .\manifest_windows.json inventory
python .\archive_windows.py --manifest .\manifest_windows.json download
python .\archive_windows.py --manifest .\manifest_windows.json status

Full official mirrors

# Every canonical/original repo from trusted official organizations
python .\discover_official_orgs.py --output .\official_org_manifest.json
python .\archive_windows.py --manifest .\official_org_manifest.json inventory
python .\archive_windows.py --manifest .\official_org_manifest.json download

# Literal everything: official quants, conversions, and duplicate formats too
python .\discover_official_orgs.py --all-formats --output .\official_everything_manifest.json
python .\archive_windows.py --manifest .\official_everything_manifest.json inventory
python .\archive_windows.py --manifest .\official_everything_manifest.json download

Targeted operations

# Tier A first
python .\archive_windows.py --manifest .\manifest_windows.json --scopes A download

# One model
python .\archive_windows.py --manifest .\manifest_windows.json --repos moonshotai/Kimi-K2.7-Code download

# Stop on first error instead of logging and continuing
python .\archive_windows.py --manifest .\manifest_windows.json download --stop-on-error

Re-run unchanged after a reboot or network failure. Completed revisions are skipped and partial Hub/Xet chunks resume.

The audited 77-model launch set

Tier A is frontier/current/unique; Tier B adds strong breadth and size tiers; Tier C is large overlap or poor bytes-per-capability. The full instruction downloads all tiers—the letters only control order.

TierOfficial repositoryCapabilityFull treeAccess / licenseCommit
Amistralai/Voxtral-Mini-4B-Realtime-2602
Current realtime Voxtral
asr16.52 GiBOpen
apache-2.0
2769294da956
Aopenai/whisper-large-v3
Canonical full-quality Whisper
asr23.01 GiBOpen
apache-2.0
06f233fe06e7
Ablack-forest-labs/FLUX.2-dev
Current high-end FLUX generation/editing; gated noncommercial
image165.44 GiBGated
other
26afe3a78bb2
Ablack-forest-labs/FLUX.2-klein-4B
Fast permissive FLUX branch
image22.11 GiBOpen
apache-2.0
e7b7dc27f91d
AQwen/Qwen-Image-2512
Current Qwen image generation
image53.74 GiBOpen
apache-2.0
25468b98e327
Atencent/HunyuanImage-3.0-Instruct
Current Hunyuan image generation/editing
image157.07 GiBOpen
other
2ec2c78bee7d
AQwen/Qwen-Image-Edit-2511
Current Qwen image editor
image-edit53.76 GiBOpen
apache-2.0
6f3ccc0b56e4
Amoonshotai/Kimi-K2.7-Code
Current Kimi coding/agent flagship
llm-code554.33 GiBOpen
other
78b5c461e9cd
AQwen/Qwen3.6-27B
Current dense Qwen coding model
llm-code51.77 GiBOpen
apache-2.0
6a9e13bd6fc8
Azai-org/GLM-5.1
Current GLM agentic engineering flagship; BF16
llm-code1.37 TiBOpen
mit
26e1bd6e011f
Aopenai/gpt-oss-20b
Current compact gpt-oss
llm-efficient38.46 GiBOpen
apache-2.0
6cee5e81ee83
Adeepseek-ai/DeepSeek-V4-Pro
Current DeepSeek flagship
llm-general805.36 GiBOpen
mit
5607980f3a4b
AMiniMaxAI/MiniMax-M3
Current MiniMax multimodal flagship; BF16 original
llm-general795.54 GiBOpen
other
81b23f9add85
Amoonshotai/Kimi-K2.6
Current Kimi general multimodal flagship
llm-general554.33 GiBOpen
other
7eb5002f6aad
Aopenai/gpt-oss-120b
Current OpenAI open-weight flagship
llm-general182.32 GiBOpen
apache-2.0
b5c939de8f75
AQwen/Qwen3.5-397B-A17B
Current large Qwen open flagship
llm-general751.41 GiBOpen
apache-2.0
8472618112ab
AACE-Step/Ace-Step1.5
Current permissive music generation
music-audio9.40 GiBOpen
mit
19671f406d60
AQwen/Qwen3-Omni-30B-A3B-Instruct
Current Qwen omni conversation/generation
omni65.69 GiBOpen
other
26291f793822
AQwen/Qwen3-TTS-12Hz-1.7B-Base
Qwen TTS foundation and voice cloning
tts4.23 GiBOpen
apache-2.0
fd4b25438912
AWan-AI/Wan2.2-TI2V-5B
Lean current Wan text/image-to-video
video31.85 GiBOpen
apache-2.0
921dbaf3f167
ALightricks/LTX-2.3
Current synchronized audio-video generation
video-audio146.22 GiBOpen
other
76730e634e70
ACohereLabs/command-a-plus-05-2026-bf16
Current Command flagship
vlm407.48 GiBOpen
apache-2.0
623c723b1f05
Agoogle/diffusiongemma-26B-A4B-it
Newest Gemma-family diffusion model
vlm48.13 GiBOpen
apache-2.0
0f28bc42f588
Ameta-llama/Llama-4-Maverick-17B-128E-Instruct
Current Llama flagship; gated
vlm748.04 GiBGated
other
73d14711bcc7
Amistralai/Mistral-Medium-3.5-128B
Current dense Mistral flagship
vlm248.89 GiBOpen
other
ed0c85631cf2
Amistralai/Mistral-Small-4-119B-2603
Current efficient Mistral general/reasoning
vlm225.28 GiBOpen
apache-2.0
19037a2b5fa9
Bopenai/whisper-large-v3-turbo
Fast Whisper variant
asr1.51 GiBOpen
mit
41f01f3fe87f
Bmistralai/Voxtral-Small-24B-2507
Larger offline Voxtral audio model
audio-understanding90.40 GiBOpen
apache-2.0
da5b42409f27
BByteDance-Seed/BAGEL-7B-MoT
Unified Seed visual generation/understanding
image27.53 GiBOpen
apache-2.0
5019f57d168e
Bstabilityai/stable-diffusion-3.5-large
Current Stability image flagship; gated
image66.67 GiBGated
other
ceddf0a7fdf2
BByteDance-Seed/VINCIE-7B
Seed in-context image editor
image-edit61.79 GiBOpen
apache-2.0
ecd9c6c6c564
BByteDance-Seed/Stable-DiffCoder-8B-Instruct
Current diffusion coding specialist
llm-code15.38 GiBOpen
mit
10cdaf9b486f
BCohereLabs/North-Mini-Code-1.0
Current Cohere coding model
llm-code56.81 GiBOpen
apache-2.0
7cbbd565c51a
Bibm-granite/granite-4.1-3b
Current compact Granite
llm-edge6.35 GiBOpen
apache-2.0
c0650403e44e
Bmeta-llama/Llama-3.2-3B-Instruct
Small official Llama tier; gated
llm-edge11.98 GiBGated
llama3.2
0cb88a4f764b
Bmicrosoft/Phi-4-mini-flash-reasoning
Compact Phi reasoning
llm-edge7.20 GiBOpen
mit
1dff8163d28e
Bmicrosoft/Phi-4-mini-instruct
Compact Phi general instruct
llm-edge7.17 GiBOpen
mit
cfbefacb9925
Bnvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
Current small dense Nemotron
llm-edge7.42 GiBOpen
other
dfaf35de3e30
BCohereLabs/tiny-aya-global
Current tiny Aya; gated noncommercial
llm-edge-multilingual6.26 GiBGated
cc-by-nc-4.0
b2d281bab0be
Ballenai/Olmo-Hybrid-Instruct-DPO-7B
Current OLMo hybrid instruct
llm-efficient13.85 GiBOpen
apache-2.0
ec62da5af010
Bdeepseek-ai/DeepSeek-V4-Flash
Efficient current DeepSeek sibling
llm-efficient148.67 GiBOpen
mit
553034d7dd9e
Bnvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
Current efficient Nemotron tier
llm-efficient58.84 GiBOpen
other
cbd3fa9f933d
Ballenai/Olmo-3.1-32B-Instruct
Current 32B OLMo instruct
llm-general60.05 GiBOpen
apache-2.0
ac0587e4a774
BByteDance-Seed/Seed-OSS-36B-Instruct
Current Seed open general model
llm-general67.35 GiBOpen
apache-2.0
497f1dca95eb
Bibm-granite/granite-4.1-30b
Current Granite flagship
llm-general53.78 GiBOpen
apache-2.0
4fae6278f713
Bmeta-llama/Llama-3.3-70B-Instruct
Best dense text-only Llama; gated
llm-general262.87 GiBGated
llama3.3
6f6073b42301
Bnvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16
Current Nemotron middle tier
llm-general230.27 GiBOpen
other
d51eab0d1f97
Btencent/Hunyuan-A13B-Instruct
Best practical current open Hunyuan LLM
llm-general149.78 GiBOpen
other
290ddb9a56ed
BCohereLabs/aya-expanse-32b
Current Aya text flagship; gated noncommercial
llm-multilingual60.17 GiBGated
cc-by-nc-4.0
b306ea27e360
Ballenai/Olmo-3-32B-Think
Current OLMo reasoning checkpoint
llm-reasoning60.05 GiBOpen
apache-2.0
ebd033e4f0b2
Bmicrosoft/Phi-4-reasoning-plus
Best text-only Phi reasoning
llm-reasoning27.31 GiBOpen
mit
69baf8528e1b
Bstabilityai/stable-audio-3-medium
Current Stable Audio; gated
music-audio9.73 GiBGated
other
71a346358b5c
Bmicrosoft/Phi-4-multimodal-instruct
Phi audio-capable multimodal model
omni11.99 GiBOpen
mit
93f923e1a772
Bnvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16
Current omni Nemotron
omni61.52 GiBOpen
other
24e67ea000b7
Bgoogle/gemma-3n-E4B-it
Mobile multimodal Gemma; gated
omni-edge14.66 GiBGated
gemma
c1221e9c62e3
Bopenbmb/MiniCPM-o-4_5
Current compact full-duplex omni model
omni-edge18.67 GiBOpen
apache-2.0
4382fcae8a55
Bfishaudio/s2-pro
Current Fish expressive TTS; custom license
tts10.26 GiBOpen
other
1de9996b6be3
Bmistralai/Voxtral-4B-TTS-2603
Current Voxtral TTS; noncommercial
tts7.49 GiBOpen
cc-by-nc-4.0
b81be46c3777
BQwen/Qwen3-TTS-12Hz-1.7B-CustomVoice
Qwen curated/custom voices
tts4.21 GiBOpen
apache-2.0
0c0e3051f131
BQwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign
Qwen voice design
tts4.21 GiBOpen
apache-2.0
5ecdb67327fd
BResembleAI/chatterbox
Current Chatterbox multilingual TTS
tts12.91 GiBOpen
mit
5bb1f6ee58e5
Btencent/HunyuanVideo-1.5
Current Hunyuan video family; bundled formats
video346.24 GiBOpen
other
9b49404b3f5d
Bgoogle/gemma-3-27b-it
Mature Gemma flagship; gated
vlm51.13 GiBGated
gemma
005ad3404e59
Bmeta-llama/Llama-4-Scout-17B-16E-Instruct
Current long-context Llama; gated
vlm202.39 GiBGated
other
92f3b1597a19
Bmicrosoft/Phi-4-reasoning-vision-15B
Latest Phi reasoning vision
vlm28.17 GiBOpen
mit
7df902e2fec3
Bgoogle/gemma-3-4b-it
Small Gemma multimodal tier; gated
vlm-edge8.05 GiBGated
gemma
093f9f388b31
Bibm-granite/granite-vision-4.1-4b
Current Granite vision
vlm-edge7.46 GiBOpen
apache-2.0
82472ca3a490
Bmistralai/Ministral-3-14B-Reasoning-2512
Current edge reasoning tier
vlm-edge51.98 GiBOpen
apache-2.0
eaabe4241136
Bmistralai/Ministral-3-3B-Reasoning-2512
Smallest current Mistral reasoning tier
vlm-edge14.37 GiBOpen
apache-2.0
942382969cbb
BCohereLabs/aya-vision-32b
Current Aya vision flagship; gated noncommercial
vlm-multilingual61.74 GiBGated
cc-by-nc-4.0
0554d6683492
Cbaidu/ERNIE-4.5-300B-A47B-PT
Large ERNIE text flagship; lower priority per byte
llm-general561.58 GiBOpen
apache-2.0
47bbaf1c9e35
Cnvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
Current Nemotron flagship but 1.02 TiB
llm-general1.02 TiBOpen
other
624ba927cfbe
CQwen/Qwen3-Omni-30B-A3B-Thinking
Optional overlapping Qwen omni reasoning variant
omni59.09 GiBOpen
other
2f443cfc4c54
CWan-AI/Wan2.2-I2V-A14B
Quality Wan image-to-video; large overlap
video117.54 GiBOpen
apache-2.0
206a9ee1b7bf
CWan-AI/Wan2.2-T2V-A14B
Quality Wan text-to-video; large overlap
video117.53 GiBOpen
apache-2.0
c8c270b13ee0
Cbaidu/ERNIE-4.5-VL-424B-A47B-PT
Large ERNIE multimodal flagship; lower priority per byte
vlm788.98 GiBOpen
apache-2.0
a3d5a8439d67
Cmistralai/Mistral-Large-3-675B-Instruct-2512-BF16
Current largest Mistral but 1.23 TiB
vlm1.23 TiBOpen
apache-2.0
d40f4a01762d

Sizes include every file in the pinned repository, not only tensor shards.

Everything mode: trusted official organizations

The crawler dynamically inventories every public model repository from these organizations. That is the durable way to capture new releases and long-tail specialists that no static list can keep current.

OpenAIMeta LlamaGoogleMicrosoftNVIDIAMistralCohereAi2IBMDeepSeekQwenMoonshotMiniMaxZ.ai / THUDMTencentByteDance SeedBaiduSalesforceTII Falcon01.AIAI21DatabricksSnowflakeNousHugging Face TBLiquid AIArceeBAAIJinaNomicMixedbreadSentence TransformersAlibaba NLPBigScienceEleutherAIBlack Forest LabsStability AIWanLightricksOpenBMBMeta vision researchControlNet / lllyasvielTencentARCOpenGVLabOpenMMLabIDEA ResearchBRIALAIONFish AudioResembleACE-StepCoquiSuno
Default crawler
All first-party repos except obvious deployment repacks.
--all-formats
Every public model repo, including official GGUF, AWQ, GPTQ, FP8, ONNX, and MLX duplicates.
Outside Hugging Face
Preserve upstream-only releases separately; never replace them with unaudited community uploads.

Notable upstream-only release: xAI Grok-1 official source and weight instructions.

Gated models and licenses

A read token is not sufficient when click-through or manual approval is required. Open the model page while signed in, accept terms, wait for approval if needed, and rerun the same command.

  • Meta Llama: manual Llama license approval.
  • Google Gemma: Gemma terms.
  • FLUX dev: gate plus non-commercial terms.
  • Stability: community-license click-through.
  • Aya: gated non-commercial variants.
!
Downloadable does not mean freely redistributable

Keep LICENSE, README, and card metadata with each snapshot. Custom terms can restrict commercial use, geography, revenue scale, or redistribution.

Verification and auditability

  1. Every launch model is pinned to a full immutable commit SHA.
  2. hf download uses content-addressed Hub/Xet transfers and resumes interrupted chunks.
  3. Each completed snapshot receives .open-model-archive.json with source, revision, expected bytes, and completion time.
  4. status reports completed and pending bytes after any interruption.
  5. Copy manifests and logs off-machine; they are the reconstruction map.
python .\archive_windows.py --manifest .\manifest_windows.json status
Get-Content "$env:OPEN_MODEL_ARCHIVE_ROOT\failures.json"
Get-ChildItem "$env:OPEN_MODEL_ARCHIVE_ROOT\models" -Recurse -Filter .open-model-archive.json | Measure-Object

Long-term operations

Monthly refresh

  1. Rerun official-org discovery to a date-stamped manifest.
  2. Diff repository IDs and revisions against the previous run.
  3. Preserve old commits when main moves; a mutable sync is not an archive.
  4. Download new revisions and protect any snapshot whose upstream disappears.

Failure policy

  • 401/403: accept terms or refresh the read token.
  • Disk full: add storage, then rerun; do not purge incomplete chunks mid-transfer.
  • Network failure: rerun unchanged.
  • Repository disappears: isolate and duplicate any completed snapshot immediately.

The honest definition of “everything”

“Every remotely good model” is not a stable finite list. The 77-row table is audited on the report date; the crawler is the reproducible long-term rule: mirror every public model released by trusted first-party labs, optionally including every format.