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liked a model about 9 hours ago
jialinyyzz/humanizer
reacted to SeaWolf-AI's post with 👍 7 days ago
🧬 Darwin-180B-RSI — an AI that learns from itself and knows when it's right 👉 https://huggingface.co/FINAL-Bench/Darwin-180B-RSI 🧬 Darwin — crossbreed and evolve the parent Darwin diagnoses strong parent models like an MRI, inherits only their best parts, and evolves the weak spots — producing a child stronger than its parents. Father model: Qwen3.8-Flash-Next (180B MoE). 🔧 Rewired paths 🔹 12 full-attention layers · 🔹 36 linear-attention layers · 🔹 48 shared-expert layers — precision-strengthened 🔒 512 routed experts · router · vision encoder — untouched → Only 0.02% of the weights changed. 🔁 RSI × 🏛️ ZTC RSI (recursive self-improvement): solve → verify against real answers → learn only the correct reasoning → repeat. ZTC (Zero-Token Confidence): reads the model's internal state once, before answering, and returns the probability the answer is right — zero extra tokens. Returns answer + confidence as JSON. {"answer": "...", "confidence": 0.97, "truncated": false} ✨ Synergy: ZTC finds where the model wavers → RSI learns exactly there → confidence gets sharper. Low confidence = stop, so agents don't act on wrong answers. ⚡ Same accuracy, 11% shorter reasoning — faster and cheaper. 📄 https://arxiv.org/abs/2605.14386 🤗 https://huggingface.co/FINAL-Bench/Darwin-180B-RSI 🏛️ https://huggingface.co/collections/FINAL-Bench/ztc-models-jev-ecosystems 🏆 The result — #1 on five Hugging Face official leaderboards 🥇 AIME 2026 100% (first perfect score on the board) 🥇 HMMT Feb 2026 100% (first perfect score on the board) 🥇 GPQA Diamond 94.44% 🥇 MMLU-Pro 88.12% 🥇 MMMU-Pro 79.48% 📏 131K-token thinking budget · bf16 · samples per benchmark listed on the model card. 🚀 #Darwin #RSI #ZTC #AIME #HMMT #GPQA #MMLUPro #MMMUPro #OpenSource
reacted to ErenAta00's post with 🔥 7 days ago
Maverick-4B-Unity-XR-Agent is now on Hugging Face. It's a 4B model that turns spoken or typed English into actions in Unity scenes. Say "put the red mug on the table" or "turn on the lamp", and it returns the tool call your app executes. If a command could mean two objects, it asks which one. If it can't do something, it says so instead of guessing. Everything runs on the user's machine through llama.cpp: no API key, no internet connection. The Q4_K_M GGUF is 2.5 GB and needs about 3 GB of GPU memory, so it fits on a 4 GB laptop GPU and usually answers in one to three seconds. It is fine-tuned from Qwen3-4B with QLoRA on about 20,000 English conversations. Results: - 83.8% on 499 human-written ALFRED instructions (right action on the right object). The base model, Qwen3-4B, scores 57.1%. The strongest of the five other models we tested, from 1.7B to 120B parameters, was Ministral 3 14B at 67.1%. - 91.7% on object types it never saw in training. - 97.3% on 440 commands run through a live Unity scene. There is also a Unity package that starts the model, describes the scene to it and carries out its tool calls. You install it from the Package Manager with a Git URL. Model: https://huggingface.co/ErenAta00/Maverick-4B-Unity-XR-Agent-GGUF Unity package: https://huggingface.co/ErenAta00/Maverick-Unity Full write-up: https://huggingface.co/blog/ErenAta00/maverick-4b-unity-xr-agent Built at the Extended Reality Laboratory (XRLab), Manisa Celal Bayar University: https://huggingface.co/ExtendedRealityLabMCBU Released under Apache-2.0. Feedback and bug reports are welcome in the Community tab.
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