Sen Ben

TURBO-BRILLIANCE 50X - Qwen 3.8 27B, LFM, and more coming soon...

15 AI CONSTRUCTS:

Each a precision engineered "consciousness" to help you in every task, using fewer tokens and generating better answers.

This model has significant internal upgrades under user control that are "on the fly" (you set in your prompt(s) and/or API).

Please read this model card carefully to get the most from it - including all the reasoning and instruct modes (27+27+9 xhigh) that feature model suggestions ("context help") for all use cases.

The Turbo Brilliance system will begin appearing in other model sizes/types shortly (IE: Qwen, Gemma, Mistral, GLM, Llama, ... including our strongest fine tunes too). The "rollout" is now in progress...

DavidAU/Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF

Qwen 3.5 9B The Defiant (1000+likes, 2m+ downloads) [ Limited 12x]:
DavidAU/Qwen3.5-9B-The-Defiant-Fable-Uncensored-Heretic-NEO-IMATRIX-MAX-MTP-GGUF

LFMs [Limited, 12x]:
DavidAU/LFM2.5-2.6B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF

DavidAU/LFM2.5-8B-A1B-Qwen3.8-Turbo-Brilliance-Power-X12-NEO-MAX-GGUF

PS: Qwen Next, LFM2.5 24B-A2B, Qwen 3.6 35B-A3B and others in testing now.

COLLECTION:
https://huggingface.co/collections/DavidAU/turbo-brilliance-ai-constructs-50x-reasoning-instruct

Please see our support page for breakdown of models (sort), info on projects and a lot more information:

DavidAU/support

reacted to SeaWolf-AI's post with 👍 14 days ago

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💻 Data-center AI, now on a laptop: POCKET-Darwin-180B

We're releasing a 4-bit GGUF build of Darwin-180B-RSI, #1 on seven official Hugging Face leaderboards (self-reported), that runs without a GPU.

📦 360 GB → 111 GB (4-bit GGUF, 4 files)
🖥️ No GPU: one server CPU (16 threads) at 18.4–21.0 tokens/s
💻 RTX 5060 laptop (8 GB VRAM) + 32 GB RAM: 4.17 tokens/s
🧊 128 GB mini PC: whole model in memory, no GPU needed
🎯 MMLU-Pro, 2,000 questions, paired: original 87.65% = 4-bit 87.65%

How?
· Only ~3B of 180B parameters are active per token (10 of 512 experts)
· llama.cpp streams just the needed experts from SSD, so 32 GB RAM is enough
· Graft quantization: we took the proven Unsloth UD-Q4_K_XL base build and swapped in only the 300 tensors our RSI training changed (300/300 verified)

Under the hood is Model-level Recursive Self-Improvement. The model solves verifiable problems, keeps only its own solutions that check out as correct, and trains on them. No human-written solutions or reasoning traces.

Built for teams that can't send data to an external cloud (defense, finance, public sector) to run a top-tier model fully offline.

📝 Article: https://huggingface.co/blog/FINAL-Bench/data-center-ai-now-on-a-laptop-pocket-darwin-180b
🤗 Model: FINAL-Bench/POCKET-Darwin-180B-GGUF
🧬 Original: FINAL-Bench/Darwin-180B-RSI

#Darwin #RSI #GGUF #llamacpp #OnDevice #MoE

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liked a model 18 days ago

ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF

Image-Text-to-Text • 177B • Updated 17 days ago • 5.35M • 908

liked a model 20 days ago

ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF

Image-Text-to-Text • 117B • Updated 17 days ago • 819k • 415

reacted to danielhanchen's post with 🔥 20 days ago

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9,098

Unsloth has surpassed 500M model downloads on Hugging Face! 🦥🤗

Qwen3.8-27B GGUF is already Unsloth’s #1 most-downloaded model ever.

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