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Papitas

NoobChanAI
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AI & ML interests

I REEALLY LIKE AI!!!

Recent Activity

new activity 17 minutes ago
DavidAU/Qwen3.6-40B-Fable-Fusion-6-Core-Deckard-Eleanor-Heretic-Uncensored-NM-DAU-NEO-MAX-MTP-GGUF:Eleanor has blown out my expectations
reacted to DavidAU's post with 🔥 about 5 hours ago
Qwen3.6-40B-Fable-Fusion-6-Core-Deckard-Eleanor-Heretic-Uncensored The fire of multiple Fable Fusion 711 cores in one, larger model. This is the first 40B fine tune that reaches "closed source" (IE OpenAI, Claude) level of intelligence in both 8 bit and 4 bit. This model is composed from multiple Qwen 27B Fable Fusion 711 cores (1700+ likes, 2.3 million+ downloads) - a record breaking model in terms of intelligence and raw power. The "40B Eleanor" takes this to the next level with improvements in thinking tokens/ thinking block size (1/10 to 1/2 the size), thinking in general and output detail quality with deep analytics too. - 1/10 to 1/2 the number of thinking tokens. - Extreme depth of detail in generations, including long form, in depth analytics. - STRONG creative abilities. - Auto-variable reasoning: Model only reasons as much as the task requires. - Strong general intelligence. - It says what it means in less words, more clearly than any previous tuned model. - It will go all in, in exacting detail when the situation calls for it. - If it thinks something is wrong / wrong path it will say so too. Regular and MTP Quants: https://huggingface.co/DavidAU/Qwen3.6-40B-Fable-Fusion-6-Core-Deckard-Eleanor-Heretic-Uncensored-NM-DAU-NEO-MAX-MTP-GGUF
reacted to Banaxi-Tech's post with 👀 about 6 hours ago
Today we wanted to release BananaMind 2 Pico, our smallest model yet at ~0.9M parameters. Instead, we accidentally ran a very expensive experiment on what happens when you push a tiny model way past its useful token budget. Short version: we trained on 200B tokens (~222K:1 tokens-per-parameter). The model peaked at 20B tokens with an INT Index of 4.55, then degraded monotonically over the next 160B to 3.31 — a 27% regression. Three of four Open SLM benchmarks were worse at the end of training than they were at 10% through. The useful compute-optimal range for Pico-tier models looks like ~22K–30K tokens per parameter. Ratios like 7K:1, 15K:1, and 22K:1 all work fine — TinyStories and most sub-3M community models sit in this range. Push much further and benchmarks start rotting. Follow us for more: https://huggingface.co/BananaMind @vovaRL @Banaxi-Tech Full writeup with all checkpoints, the Chinchilla-ratio control run, and the schedule-vs-overtraining analysis: https://huggingface.co/blog/Banaxi-Tech/ovdadadadd And if anyone, i dont know the reason why you would, wants the 20B token checkpoint reply and ill upload it as BananaMind 2.1 Pico EXP
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