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Robert Goolsby

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reacted to SeaWolf-AI's post with 👍 about 5 hours ago
💻 Data-center AI, now on a laptop: POCKET-Darwin-180B We're releasing a 4-bit GGUF build of Darwin-180B-RSI, #1 on seven official Hugging Face leaderboards (self-reported), that runs without a GPU. 📦 360 GB → 111 GB (4-bit GGUF, 4 files) 🖥️ No GPU: one server CPU (16 threads) at 18.4–21.0 tokens/s 💻 RTX 5060 laptop (8 GB VRAM) + 32 GB RAM: 4.17 tokens/s 🧊 128 GB mini PC: whole model in memory, no GPU needed 🎯 MMLU-Pro, 2,000 questions, paired: original 87.65% = 4-bit 87.65% How? · Only ~3B of 180B parameters are active per token (10 of 512 experts) · llama.cpp streams just the needed experts from SSD, so 32 GB RAM is enough · Graft quantization: we took the proven Unsloth UD-Q4_K_XL base build and swapped in only the 300 tensors our RSI training changed (300/300 verified) Under the hood is Model-level Recursive Self-Improvement. The model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces. Built for teams that can't send data to an external cloud (defense, finance, public sector) to run a top-tier model fully offline. 📝 Article: https://huggingface.co/blog/FINAL-Bench/data-center-ai-now-on-a-laptop-pocket-darwin-180b 🤗 Model: https://huggingface.co/FINAL-Bench/POCKET-Darwin-180B-GGUF 🧬 Original: https://huggingface.co/FINAL-Bench/Darwin-180B-RSI #Darwin #RSI #GGUF #llamacpp #OnDevice #MoE
repliedto pollix's post 1 day ago
stuntd 0.1.2 is out 🎉 stuntd sits in front of your LLM, learns its typed decisions and answers the confident ones locally with a small head on the Laya encoder by @convaiinnovations. About 20ms on GPU and 60ms on CPU, and anything it isn't sure about still goes to the big model. New in 0.1.2: - decisions with several fields, like category + urgency + needs_human in one call, answered locally only when every field is sure - the Anthropic Messages API learns too, not only OpenAI - auto_retrain: the daemon retrains a site in the background once enough new traffic comes in, so collect, train, shadow and live run on their own - serve --lazy loads the checkpoint on the first request Try it in the browser: https://huggingface.co/spaces/pollix/stuntd Code: https://github.com/bladedevoff/stuntd ``` pip install -U stuntd ```
reacted to sergiopaniego's post with 🔥 1 day ago
catching up on some bookmarked reads from the summer, reading Antidoom from @liquidai small reasoning models get stuck more easily when the task involves a long thinking trace and a hard problem. It starts repeating the same word over and over again ("Wait", "Alternatively"…), each repetition makes the next one likelier, and the generation is spent before it reaches an answer they measured it, 10.2% of completions for an early LFM2.5-2.6B checkpoint and 22.9% for Qwen3.5-4B at greedy. After training those drop to 1.4% and 1.0% the fix is FTPO (final token preference optimization). What I like is how narrow it is, it only touches the single token where the loop starts three ways it differs from DPO: > trains one token position, mid-generation, instead of whole sequences > spreads probability across ~20 plausible alternatives instead of swapping one overtrained token for another > keeps the regularizer in logit space, no softmax, so the rest of the vocabulary stays put the third one is what makes it usable. If you want to edit one position without disturbing the model, you can't have a loss that reshuffles the other 150k logits on the way and their explanation abt the result: the training teaches the model nothing new about math or code, it clears the failure mode that was blocking answers the model could already produce full blog > https://www.liquid.ai/blog/antidoom FTPO itself comes from Antislop, where it was built to strip overused phrasing. LiquidAI retargeted it to doom loops and under the hood it's a subclass of TRL's DPOTrainer with compute_loss overridden, around 90 lines of loss and no new trainer we documented that pattern in TRL's docs https://huggingface.co/docs/trl/main/en/customization#change-the-training-objective
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