A model is an AI’s “brain file.” ChatGPT and Claude also answer by running files like these, and many models are published so anyone can download one and run it on their own machine. These are the ones trending on Hugging Face, a site where people share models.
Likes is how many people clicked like; downloads is how many times it was fetched. In the grey line, “built on” names the model it was made from, and “params” is the size of the brain — bigger tends to mean smarter, and heavier.
What turned up in the last three days — collected by machine, published by hand.
What gets picked here ends up in What I took apart →
Collected 2026-10-15 23:01 UTC · 44 kept from 418
text-generationtransformersSource Hugging Face
LiquidAI/LFM2.5-24B-A2B386 likes6.3k downloads
otherbuilt on LFM2.5-24B-A2B-Base
What we saw
This source gives a title and a link, nothing to quote.
They claim — Best-in-class agent: Competitive with Qwen3.6-35B-A3B, a model with ~50% more total and active parameters, on agentic and tool-use tasks, scoring 48.09 on LiveClaw versus 38.19.
Published 2026-10-12
Fetched 2026-10-13 23:01 UTC · c579ed3cfe2e10ec
Torn from — the model card
image-text-to-texttransformersSource Hugging Face
openbmb/MiniCPM-V-4.7-35B-A3B790 likes9.3k downloads
apache-2.0
What we saw
This source gives a title and a link, nothing to quote.
They claim — With our Lookahead Expert Scheduling (LES) technique introduced alongside MiniCPM-V 4.7, combined with fine-grained layer-wise quantization and DSpark speculative sampling integration, Mi…
Published 2026-10-07
Fetched 2026-10-13 23:01 UTC · 5ca3d9ef5e7bf155
Torn from — the model card
text-to-speechsafetensorsSource Hugging Face
fishaudio/drama-3467 likes4.0k downloads
other
What we saw
This source gives a title and a link, nothing to quote.
They claim — A single stream gets its first audio in under 100 ms.
Published 2026-10-01
Fetched 2026-10-14 23:02 UTC · ea49f1b15998335c
Torn from — the model card
zero-shot-classificationtransformersSource Hugging Face
vllm-sr/d3197 likes2.7k downloads
apache-2.0built on Qwen3.8-27B
What we saw
This source gives a title and a link, nothing to quote.
They claim — Speed: text requests take a median of 54.4 ms, requests with an image a median of 290 ms, and requests with a 10-second video a median of 500 ms, on one AMD Instinct MI325X, one request a…
Published 2026-10-10
Fetched 2026-10-13 23:01 UTC · 01560164c36d18da
Torn from — the model card
image-text-to-texttransformersSource Hugging Face
openbmb/MiniCPM-V-4.7-1B360 likes5.3k downloads
apache-2.0
What we saw
This source gives a title and a link, nothing to quote.
They claim — The 1B variant of MiniCPM-V 4.7 advances the performance–efficiency Pareto frontier for MLLMs.
Published 2026-10-06
Fetched 2026-10-13 23:01 UTC · d1276768476a030a
Torn from — the model card
text-classificationtransformersSource Hugging Face
autotrust/JEV-35B-NVFP4306 likes33k downloads
apache-2.0built on JEV-35B
What we saw
This source gives a title and a link, nothing to quote.
They claim — Speed: the same latency as bf16 on one B200 for single requests; the gain is memory (one 32–48 GB GPU is enough).
Published 2026-10-11
Fetched 2026-10-13 23:01 UTC · b0be99502d578ee1
Torn from — the model card
image-to-imagediffusersSource Hugging Face
black-forest-labs/flux-3-image-klein201 likes3.2k downloads
other
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-10-13
Fetched 2026-10-15 23:01 UTC · 722181fb8cc2c805
Torn from — the listing only (the original would not open)
image-text-to-texttransformersSource Hugging Face
meta-models/Muse-Spark-Open-816B-A42B212 likes402 downloads
other
What we saw
This source gives a title and a link, nothing to quote.
Published 2026-09-15
Fetched 2026-10-15 23:01 UTC · 3110f3e1a27a59dd
Torn from — the listing only (the original would not open)
Nothing on this page matches what you picked.
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.