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textilelabs

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textilelabs

AI & ML interests

Independent builder training small/efficient models from scratch on consumer hardware.

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reacted to appvoid's post with 🚀 3 days ago

You know, I can't believe we are actually doing it, literally advancing the frontier at the edge! We've come a long way since the first models on open slm leaderboard, and, as many things that change, performance is one of the main things that has exploded. 7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like. All I can say is that the best is yet to come. And will be agentic.

upvoted a changelog 3 days ago

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upvoted a changelog 7 days ago

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reacted to appvoid's post with 🚀 3 days ago

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3,932

You know, I can't believe we are actually doing it, literally advancing the frontier at the edge! We've come a long way since the first models on open slm leaderboard, and, as many things that change, performance is one of the main things that has exploded.

7 years ago, the best we had was GPT-2. Today, we got 90m models beating smollm2! I can't barely see what a future with increasingly powerful small models will look like.

All I can say is that the best is yet to come. And will be agentic.

11 replies

upvoted a changelog 3 days ago

Hugging Face Changelog

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7 days ago

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upvoted a changelog 7 days ago

Hugging Face Changelog

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12 days ago

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reacted to Banaxi-Tech's post with 🔥 18 days ago

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3,230

We have released BGA!
And wow, It provides 256x (and 512x at the end of 1M) yes 256x LESS attention compute at 1M context window.
That means you can train a 1M context window at the compute of a ~4K context window.

Check IT OUT: BananaMind/blog

The Accuracy Should BE WAy better than DSA but untested yet.

And, now some updates on BananaMind 3:

BananaMind 3 Will start training Soon!
Sizes: 10M, 25M, 50M, 100M, 150M

And the context windows ARE INSANE: 10M, 16K context, 25M 16k context, 50M 32K context, 100M and 150M, 64K context!!!!

13 replies

upvoted a changelog 19 days ago

Hugging Face Changelog

Decision Models on the Hub

19 days ago

• 228

reacted to Undi95's post with 🚀🔥 22 days ago

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4,548

Yo, I'm back, and I'm currently trying to teach a local LLM to stop waiting for a prompt kek.

I'm building a small proof of concept: can an open-weight model (Qwen3.8-27B, running locally on 2 RTX 5090 GPUs) learn to direct itself, then improve from its own exploration, without a human in the loop and without breaking it for normal use?

No user, no task. The model only gets observations from its environment. Each turn, it writes its own agenda (goal/open questions/next step), then picks an action: search the web, read a page, or take a note.
The environment is the judge, not another LLM. A note is accepted only if it quotes the page it read word for word. Facts are checked by exact match.
Later, code will be checked by actually running tests.

The best episodes become fine-tuning data (LoRA). The helper system prompt is removed at training time, so the behavior has to live in the weights.
Each new model goes through a fixed benchmark gate: math, general knowledge, "does it still answer humans normally?", autonomy, and learned facts on held-out sources. It's kept only if nothing regresses, otherwise it's discarded. Then the loop starts again.

The full pipeline works end to end: collect, train, merge, deploy, benchmark. The baseline is clear. Without any instructions, the base model's real autonomy is zero: it behaves like a chatbot waiting for a question. That's the number this small project is trying to move.

I haven't found a public tool that runs this whole loop (self-directed exploration, verifiable rewards, continual fine-tuning and a regression gate) on home hardware. The goal isn't AGI in a bedroom. It's to show that anyone can try it, measure it honestly, and see where it breaks.

Code and results will be released once the first real iterations are done. At the moment the code is... running, but made with scotch and stick, still only a PoC I want to try.

Did you already tried something like that? What was your result? I'm curious!

23 replies

updated a collection 22 days ago

Loom-Weave

Collection

The Loom-Weave collection. These models are our largest models. Tier 3 (heavy). • 3 items • Updated 22 days ago

updated a model 22 days ago

textilelabs/Loom-Weave-4

Text Generation • 55.6M • Updated 22 days ago • 497

published a model 22 days ago

textilelabs/Loom-Weave-4

Text Generation • 55.6M • Updated 22 days ago • 497

upvoted a changelog 23 days ago

Hugging Face Changelog

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23 days ago

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New activity in hugging-science/ripp 23 days ago

WAKE UP

#5 opened 23 days ago by

GGUFGuy

reacted to Banaxi-Tech's post with 🚀 25 days ago

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5,168

ACR 1.0 launch is being prepared and researched now!
Also I'm going to vacation tomorrow but it should still be released!

saicr

3 replies

reacted to cafkafk's post with 🔥 26 days ago

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4,501

I'm working on a local model that can be run on stuff like an an RTX 3080 at 60 t/s. I'm focused on making it good at Rust and Nix. It's... not done yet, but doing great already. Had a 28/100 on livebench v6 earlier, which obviously isn't amazing.

But for my first real model, very exciting. Can't wait to share more as I get closer to the finish line in the coming weeks.