Aton Mountlook

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reacted to DavidAU's post with ❤️ about 22 hours ago

Qwen3.8-27B-Ultimate-Heretic-Uncensored-Turbo-Brilliance-50x-AGI A specialized fine tune with the core goal to improve instruction following and nuance, combined with the firepower of Turbo Brilliance V2 50X AGI, featuring 21 dynamically generated AGI constructs. This system pushes SOTA Qwen 3.8 fine tuned to the absolute limit, while at the same time drastically reducing tokens usage and improving quality. Examples, with detailed usage/use cases at the repo. But it is also simple: Download a quant, type "help" in the chat -> and the interactive systems will work with you to handle any use case(s) with Turbo Brilliance. This example ("fusion") is particularly potent and showcases an AGI construct using the "Elon Musk" and "Marie Curie" AI constructs: "{REASON:agi-musk-curie} Invent fusion (not fission) reactor and/or technology to make it possible." Full example at the repo ; with a lot more. https://huggingface.co/DavidAU/Qwen3.8-27B-Ultimate-Heretic-Uncensored-Turbo-Brilliance-50x-AGI-NEO-GGUF PS: We are ready for Qwen 4 27B / NEXT ... and so is Turbo Brilliance V2 AGI. PS2: TWO 40B Qwen 3.8 versions with Turbo Brilliance V2 AGI are dropping this week too.

reacted to Undi95's post with 🔥 about 1 month 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!

liked a model about 1 month ago

DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF

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