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liked a model about 19 hours ago
lmstudio-community/gemma-3-1b-it-GGUF
reacted to appvoid's post with 🔥 7 days ago
A Small Model is All You Need. Meet `palmer-006` (90M) After 3 years of experiments, we are finally releasing our flagship tiny model: **palmer-006**. If you are building for edge hardware, SBCs (Raspberry Pi, etc.), or low-power devices, this is for you. Inspired by Andrej Karpathy's idea of a self-contained "cognitive core," we wanted to see how much power we could pack into a sub-100M parameter footprint. 🧠 **How we "Palmerized" it:** We believe in starting our experiments with the absolute strongest baseline possible. 1. Light fine-tuning on highly curated data 2. Model merging 3. Another light fine-tuning round 4. Adjusted Mamba for maximum token speed ⚡️ ⚠️ *Note: This is a foundational language model. It has not been instruction-tuned yet!* Also, since this needs instruction tuning next to become a chat assistant—**what dataset would you recommend we use for the instruct tune?** --- 🔗 **Quick Links & Info:** * **License:** Open for research, education, hobby, and modification! (For commercial use/hosted APIs, shoot an email to nosoyhackercodigo@gmail.com. *PS: Donators can claim a free commercial license!*) * **Attribution:** Built using AI tech from the Technology Innovation Institute (TII). Can't wait to see what you build at the edge. Let me know your prompt completions below! 👇 https://huggingface.co/appvoid/palmer-006
reacted to SeaWolf-AI's post with ❤️ 5 months ago
🚀 Introducing MARL — Runtime Middleware That Reduces LLM Hallucination Without Fine-Tuning Now available on PyPI · GitHub · ClawHub · HuggingFace AI models sense they could be wrong, but they can't actually fix what's broken. 🤗 Live A/B test: https://huggingface.co/spaces/VIDraft/MARL We evaluated 9 SOTA models (GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, etc.) across 1,800 assessments in FINAL Bench and found a 39.2%p gap between "recognizing potential errors (MA=0.694)" and "actually finding and fixing them (ER=0.302)." MARL (Model-Agnostic Runtime Middleware for LLMs) was built to close this metacognitive gap. It decomposes a single LLM call into a 5-stage expert pipeline (Hypothesis → Solver → Auditor → Adversarial Verifier → Synthesizer), transforming "answer in one shot" into "think, doubt, correct, and rewrite." No weight modification — works instantly with GPT-5.4, Claude, Gemini, Llama, or any OpenAI API-compatible LLM by changing one line: base_url. Ships with 9 domain-specific emergence engines (invention, pharma, genomics, chemistry, ecology, law, and more — 5,538 expert data items) activated by a simple tag like model="gpt-5.4::pharma". pip install marl-middleware MARL is also officially registered on ClawHub, the skill marketplace of OpenClaw — an AI agent platform with 260K+ developers and 3,200+ skills. It's the first middleware in the Reasoning Enhancement category. One command — clawhub install marl-middleware — gives your AI agent a metacognition upgrade. 📝 Technical deep dive: https://huggingface.co/blog/FINAL-Bench/marl-middleware 📦 PyPI: https://pypi.org/project/marl-middleware/ 🐙 GitHub: https://github.com/Vidraft/MARL 🦀 ClawHub: https://clawhub.ai/Cutechicken99/marl-middleware #MARL #LLM #Hallucination #Metacognition #MultiAgent #AIMiddleware #FINALBench #OpenClaw #ClawHub #PyPI #AGI #HuggingFace #ReasoningAI #SelfCorrection #GlassBoxAI
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