Hugging Face's logo
Loom's picture
🚀 Launched Weave 4!

Loom

textilelabs
6 3 7
P(doom) 1%
Stevve2's profile picture webbrain-a02's profile picture CodeSoft's profile picture
·

AI & ML interests

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

Recent Activity

reacted to Banaxi-Tech's post with 🔥 1 day ago
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: https://huggingface.co/spaces/BananaMind/blog#bananamind-gate-attention 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!!!!
upvoted a changelog 2 days ago
Decision Models on the Hub
reacted to Undi95's post with 🚀 5 days ago
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!
View all activity

Organizations

None yet