I have hit 300 followers, and I think this calls for a bit of a giveaway π a unique one, too. I have had countless AI projects I have wanted to make but have been (brutally) blocked by compute. Now that I finally have just enough compute to sort of get around (i still don't have enough π) and for hitting 300 followers (tysm!) I will be funding three of the communities projects via HuggingFace jobs, giving them 150 dollars max worth of compute each. I will personally be picking the winners, I am looking for projects that genuinely hit the compute wall: great ideas, blocked by compute, just like the countless ideas I've had. To join, head over to https://giveaway.ssh.codes RULES: - Final result must be open weight or open source - Only one submission per person - Have fun!
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.
π§ **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 β‘οΈ
β οΈ *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 assistantβ**what dataset would you recommend we use for the instruct tune?**
--- π **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! π
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 β Solver β Auditor β Adversarial Verifier β Synthesizer), transforming "answer in one shot" into "think, doubt, correct, and rewrite."
No weight modification β 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 β 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 β an AI agent platform with 260K+ developers and 3,200+ skills. It's the first middleware in the Reasoning Enhancement category. One command β clawhub install marl-middleware β gives your AI agent a metacognition upgrade.
β¨ Big wave of foundation models: still scaling, but efficiency, reasoning, and deployment now matter more than size - DeepSeek-V3.2 - Z.ai GLM-4.7 - MiniMax-M2.1 - Xiaomi: MiMo-V2-Flash
β¨ Multimodal reasoning is now default - Z.ai GLM-4.6V - Z.ai AutoGLM-Phone 9B - Bytedance: Dolphin-v2