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mayafree
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reacted to SeaWolf-AI's post with ๐Ÿ”ฅ about 13 hours ago
๐Ÿ”ฅ 128 Blackwell GPUs โ€” Thank You, Hugging Face I've been awarded 128 NVIDIA Blackwell GPUs through NIPA (Korea's National IT Industry Promotion Agency). Sharing this here first โ€” because Hugging Face is where it all started. I design LLM architectures from scratch. HF was my lab โ€” dissecting Transformers internals, analyzing thousands of checkpoints, iterating on Spaces with global feedback. Our FINAL Bench reached #5 globally in HF dataset popularity, and this research is exactly what earned the GPU grant. ๐Ÿ‘‰ https://huggingface.co/spaces/FINAL-Bench/Leaderboard These 128 Blackwells will scale AETHER-Net โ€” our Proto-AGI architecture (Emergence Engine ยท Meta-Cognition ยท SLAI ยท Multi-Intelligence ยท Synergy & Critique) โ€” validated at 0.8B with MoE expansion to 2.1B params. Next stop: 166B. People I must thank: @John6666 โ€” Guardian of this ecosystem. Never misses a forum question, interested in every project, active 24/7. I've genuinely wondered if you're a machine. Remarkable. @bartowski โ€” Master of quantization. The hidden infrastructure of open-source LLM. Countless experiments possible thanks to you. @SaylorTwift โ€” You see what others miss. Insight that cuts to the essence. Deep respect. My promise: AETHER-Net design docs, training recipes, checkpoints, and failure logs โ€” all shared here openly. ๐Ÿค— Thank you, Hugging Face. Let's turn the next page together. ๐Ÿš€ vidraft ยท VIDRAFT #OpenScience #HuggingFace #ProtoAGI #AETHER #LLMArchitecture #Blackwell #NIPA
reacted to SeaWolf-AI's post with ๐Ÿš€ 1 day ago
๐Ÿ’Ž Gemma 4 Playground โ€” Dual Model Demo on ZeroGPU We just launched a Gemma 4 Playground that lets you chat with Google DeepMind's latest open models โ€” directly on Hugging Face Spaces with ZeroGPU. https://huggingface.co/spaces/FINAL-Bench/Gemma-4-Multi ๐Ÿ‘‰ Try it now: FINAL-Bench/Gemma-4-Multi Two Models, One Space Switch between both Gemma 4 variants in a single interface: โšก Gemma 4 26B-A4B โ€” MoE with 128 experts, only 3.8B active params. 95% of the 31B's quality at ~8x faster inference. AIME 88.3%, GPQA 82.3%. ๐Ÿ† Gemma 4 31B โ€” Dense 30.7B. Best quality among Gemma 4 family. AIME 89.2%, GPQA 84.3%, Codeforces 2150. Arena open-model top 3. Features Vision โ€” Upload images for analysis, OCR, chart reading, document parsing Thinking Mode โ€” Toggle chain-of-thought reasoning with Gemma 4's native <|channel> thinking tokens System Prompts โ€” 6 presets (General, Code, Math, Creative, Translate, Research) or write your own Streaming โ€” Real-time token-by-token response via ZeroGPU Apache 2.0 โ€” Fully open, no restrictions Technical Details Built with the dev build of transformers (5.5.0.dev0) for full Gemma 4 support including multimodal apply_chat_template, variable-resolution image processing, and native thinking mode. Runs on HF ZeroGPU with @spaces.GPU โ€” no dedicated GPU needed. Both models support 256K context window and 140+ languages out of the box. Links - ๐Ÿค— Space: [FINAL-Bench/Gemma-4-Multi](https://huggingface.co/spaces/FINAL-Bench/Gemma-4-Multi) - ๐Ÿ“„ Gemma 4 26B-A4B: [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) - ๐Ÿ“„ Gemma 4 31B: [google/gemma-4-31B-it](https://huggingface.co/google/gemma-4-31B-it) - ๐Ÿ”ฌ DeepMind Blog: [Gemma 4 Launch](https://deepmind.google/blog/gemma-4-byte-for-byte-the-most-capable-open-models/)
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