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reacted to Parveshiiii's post with 🔥 8 days ago
Just did something I’ve been meaning to try for ages. In only 3 hours, on 10 billion+ tokens, I trained a custom BPE + tiktoken-style tokenizer using my new library microtok — and it hits the same token efficiency as Qwen3. Tokenizers have always felt like black magic to me. We drop them into every LLM project, but actually training one from scratch? That always seemed way too complicated. Turns out it doesn’t have to be. microtok makes the whole process stupidly simple — literally just 3 lines of code. No heavy setup, no GPU required. I built it on top of the Hugging Face tokenizers library so it stays clean, fast, and actually understandable. If you’ve ever wanted to look under the hood and build your own optimized vocabulary instead of just copying someone else’s, this is the entry point you’ve been waiting for. I wrote up the full story, threw in a ready-to-run Colab template, and dropped the trained tokenizer on Hugging Face. Blog → https://parveshiiii.github.io/blogs/microtok/ Trained tokenizer → https://huggingface.co/Parveshiiii/microtok GitHub repo → https://github.com/Parveshiiii/microtok
reacted to SeaWolf-AI's post with 🔥 about 1 month ago
ALL Bench — Global AI Model Unified Leaderboard https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard If you've ever tried to compare GPT-5.2 and Claude Opus 4.6 side by side, you've probably hit the same wall: the official Hugging Face leaderboard only tracks open-source models, so the most widely used AI systems simply aren't there. ALL Bench fixes that by bringing closed-source models, open-weight models, and — uniquely — all four teams under South Korea's national sovereign AI program into a single leaderboard. Thirty-one frontier models, one consistent scoring scale. Scoring works differently here too. Most leaderboards skip benchmarks a model hasn't submitted, which lets models game their ranking by withholding results. ALL Bench treats every missing entry as zero and divides by ten, so there's no advantage in hiding your weak spots. The ten core benchmarks span reasoning (GPQA Diamond, AIME 2025, HLE, ARC-AGI-2), coding (SWE-bench Verified, LiveCodeBench), and instruction-following (IFEval, BFCL). The standout is FINAL Bench — the world's only benchmark measuring whether a model can catch and correct its own mistakes. It reached rank five in global dataset popularity on Hugging Face in February 2026 and has been covered by Seoul Shinmun, Asia Economy, IT Chosun, and Behind. Nine interactive charts let you explore everything from composite score rankings and a full heatmap to an open-vs-closed scatter plot. Operational metrics like context window, output speed, and pricing are included alongside benchmark scores. All data is sourced from Artificial Analysis Intelligence Index v4.0, arXiv technical reports, Chatbot Arena ELO ratings, and the Korean Ministry of Science and ICT's official evaluation results. Updates monthly.
reacted to SeaWolf-AI's post with 🔥 about 1 month ago
FINAL Bench Released: The Real Bottleneck to AGI Is Self-Correction We release FINAL Bench, the first benchmark for measuring functional metacognition in LLMs — the ability to detect and correct one's own reasoning errors. Every existing benchmark measures final-answer accuracy. None measures whether AI knows it is wrong. Dataset: [FINAL-Bench/Metacognitive](https://huggingface.co/datasets/FINAL-Bench/Metacognitive) | 100 Tasks | 15 Domains | 8 TICOS Types | Apache 2.0 Leaderboard: https://huggingface.co/spaces/FINAL-Bench/Leaderboard Article: https://huggingface.co/blog/FINAL-Bench/metacognitive Core Innovation Our 5-axis rubric separates what no prior benchmark could: MA (Metacognitive Accuracy) — the ability to say "I might be wrong", and ER (Error Recovery) — the ability to actually fix it. This maps directly to the monitoring-control model of Nelson & Narens (1990) in cognitive psychology. Three Findings Across 9 SOTA Models We evaluated GPT-5.2, Claude Opus 4.6, Gemini 3 Pro, DeepSeek-V3.2, Kimi K2.5, and others across 100 expert-level tasks: 1. ER Dominance. 94.8% of MetaCog gain comes from Error Recovery alone. The bottleneck to AGI is not knowledge or reasoning — it is self-correction. 2. Declarative-Procedural Gap. All 9 models can verbalize uncertainty (MA = 0.694) but cannot act on it (ER = 0.302). They sound humble but fail to self-correct — the most dangerous AI safety profile. 3. Difficulty Effect. Harder tasks benefit dramatically more from metacognition (Pearson r = -0.777, p < 0.001). ```python from datasets import load_dataset dataset = load_dataset("FINAL-Bench/Metacognitive", split="train") ``` Paper: FINAL Bench: Measuring Functional Metacognitive Reasoning in LLMs FINAL Bench is the first tool to tell apart what AI truly knows from what it merely pretends to know.
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