Hugging Face
Models
Datasets
Spaces
Buckets
new
Docs
Enterprise
Pricing
Website
Tasks
HuggingChat
Collections
Languages
Organizations
Community
Blog
Posts
Daily Papers
Hardware
Learn
Discord
Forum
GitHub
Solutions
Team & Enterprise
Hugging Face PRO
Enterprise Support
Inference Providers
Inference Endpoints
Storage Buckets
Log In
Sign Up
Mario Hoffmann
ReingeFallen
23
1
2
Follow
webbrain-one-739284's profile picture
EngineerFabrice's profile picture
webxos's profile picture
3 followers
·
91 following
AI & ML interests
None yet
Recent Activity
replied
to
Undi95
's
post
21 minutes 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!
new
activity
about 24 hours ago
ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ:
Kimi K3 GSQ-RCO-GGUF IQ1_S, IQ1_M, IQ2_XXS
new
activity
1 day ago
LessThanThreeAI/Qwen3.8-27B-Humanlike-Chat-GGUF:
vision tower?
View all activity
Organizations
None yet
models
1
ReingeFallen/DeepSeek-V4-Flash-Base-gguf
284B
•
Updated
10 days ago
•
742
datasets
0
None public yet