Models

What turned up in the last three days — collected by machine, published by hand.

What gets picked here ends up in Teardowns →

Collected 2026-09-08 23:01 UTC · 46 kept from 280

Terms on this pageMixture of Experts Streaming GGUF FP8 All terms →

text-generationtransformersSource Hugging Face

openbmb/MiniCPM5-2B

652 likes2.9k downloads

apache-2.0

What we saw

This source gives a title and a link, nothing to quote.

They claim — It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.

Published 2026-09-06

Fetched 2026-09-07 23:01 UTC · 80cbd6d3b1d56d8d

Torn from — the model card

text-generationsafetensorsSource Hugging Face

dealignai/GLM-5.3-CYBERSECURITY-FP8

313 likes19k downloads

mitbuilt on GLM-5.3-FP8

What we saw

This source gives a title and a link, nothing to quote.

They claim — 131k context works on 8× H200 at max-num-seqs 24 (≈2.98× concurrency headroom).

Published 2026-08-30

Fetched 2026-09-06 23:01 UTC · 906a40b5c2f6096f

Torn from — the model card

image-to-videominimax-h3Source Hugging Face

WarmBloodAban/Minimax-h3_Singularity

172 likes58k downloads

apache-2.0

What we saw

This source gives a title and a link, nothing to quote.

They claim — 🛡️ Full Base Capability Retention: 100% preserves MiniMax-H3's original prompt adherence, style adaptability, and base multimodal generation strength.

Published 2026-09-05

Fetched 2026-09-06 23:01 UTC · 740cf7d9fea002ec

Torn from — the model card

automatic-speech-recognitiontransformersSource Hugging Face

microsoft/VibeVoice-ASR-Streaming-7B

153 likes1.4k downloads

mit

What we saw

This source gives a title and a link, nothing to quote.

Published 2026-09-02

Fetched 2026-09-06 23:01 UTC · 52ccca433ca25fdf

Torn from — the model card

text-generationtransformersSource Hugging Face

openbmb/MiniCPM5-2B-GGUF

117 likes7.8k downloads

apache-2.0

What we saw

This source gives a title and a link, nothing to quote.

They claim — It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA.

Published 2026-09-05

Fetched 2026-09-08 16:21 UTC · 8d445192f84dfbcc

Torn from — the model card

video-to-videodiffusersSource Hugging Face

Viggle/Viggle-Animate

117 likes0 downloads

otherbuilt on MiniMax-H3

What we saw

This source gives a title and a link, nothing to quote.

They claim — One B200, 480×832, 124 frames at 24 fps, bf16, no compile, no offload.

Published 2026-08-31

Fetched 2026-09-07 23:01 UTC · 4eb05306a3d39be5

Torn from — the model card

text-generationtransformersSource Hugging Face

IFM/K2-Horizon-7B

96 likes3.0k downloads

apache-2.0

What we saw

This source gives a title and a link, nothing to quote.

Published 2026-09-01

Fetched 2026-09-07 23:01 UTC · 07b1ce9c36dd2f9f

Torn from — the model card

text-to-speechsafetensorsSource Hugging Face

phasefield-audio/Irodori-TTS-v4.1-Anime

94 likes0 downloads

mitbuilt on Irodori-TTS-v4.1-Small

What we saw

This source gives a title and a link, nothing to quote.

Published 2026-09-04

Fetched 2026-09-08 16:21 UTC · eb882273c411c5c7

Torn from — the model card

Papers, repos, models, blogs, Reddit and podcasts — once a day, by machine. Everything collected gets taken apart once — the README, the abstract, the model card — and what that turns up is kept for good, whether or not it becomes a card. Titles and links go to the original; nothing is reproduced here.

The grey line on each card is what the teardown found, not what the source advertises: the licence, the last commit, the model it was built on, whether the paper states its own limits. Open “What we saw” to check what was read to get it. Where it says listing only, the original could not be read — some sites refuse us, and we do not pretend otherwise.

A line in quotation marks is a sentence lifted from the source itself — their number, not a measurement of ours. We copy it and leave it unchecked; that is the point of quoting it.

Topics are assigned by matching words in the original title, quote and tags against a fixed list — no model reads the card. Some cards match nothing and carry no topic at all; pick a topic and those drop out. Source and topic narrow together.

Each card keeps the sentence we actually read at the moment we read it, plus a fingerprint of the response we received. That sentence — not the whole page — is what we can still stand behind if the original later changes or disappears.