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AI 모델 85003

• minimax-h3-turbo-GGUF↗

  ChrisColeTech

  The MiniMax H3 **FL2VA (pruned)** DiT with the **LightX2V Turbo 8-step v1.0** LoRA **baked in**. The MiniMax H3 **REF2VA (pruned)** DiT with the **LightX2V Turbo 4-step v1.0** LoRA **baked in**. Both samples use the **same** turbo **Q4_K_M turbo GGUF** diffusion model, prompt, and settings, only the **text encoder** changes (4B vs 8B).

• Wan2.1-Fun-14B-InP-gguf↗

  city96

• audioseal-GGUF↗

  cstr

• Spaetzle-v60-7b↗

  cstr

• Qwen3.8-Flash-Next-Abliterated-Uncensored-Turbo-Brilliance-50x-Reasoning-Instruct-GSQ-RCO-GGUF↗

  DavidAU

  IMPORTANT: 120B parameter model, no nanny with 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). (TIP: You can run this model on 12GB GPUs and up too "at speed".) Qwen3.8-Flash-Next-Abliterated-Uncensored-Turbo-Brilliance-50x-Reasoning-Instruct-GSQ-RCO-GGUF - 180 Billion parameters with 512 experts, 10 activated per token. - Turbo Brilliance: Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. - SOTA BF16 performance in "IQ3_S" using quant tuning stronger than QAT by GSQ-RCO (ISTA-DASLab) - Speed TEST: 46 GB on 2 GPUS + offload onto ram: 17 t/s ; speed at full offload (to GPUs) : 60-90 t/s + depending on GPU[s] / setup etc etc. || RTX6000 [96 GB VRAM] :150 t/s (estimated based on avail data)+ ; STRATA will be even faster / can be used on 12GB VRAM cards and up. - Example generations below [4], including a "business plan" (from AI Construct "Business"). - Token reduction (thinking) when using AI CONSTRUCTS (preloads at 1000+ t/s) : EXTREME ; instruct modes: 0 thinking tokens. - No nanny, no censorship ; this is the abliterated version. - Abliteration of GSQ-RCO quant by "SC117" (see model tree, visit repo for info) ; did an outstanding job!

• Qwen3-Embedding-0.6B-GGUF↗

  dengcao

• Qwen.Qwen3-VL-4B-Instruct-GGUF↗

  DevQuasar

• GAP-models↗

  elichen-skymizer

  This repository backs up the retained local models used for GAP reference generation and quantization evaluation. It contains multiple model families and checkpoints. Select an individual checkpoint and its projector when loading a model. The directory layout preserves the original model-family/provider paths. The two Kimi checkpoints keep their separate directories, conversion records, and original Hugging Face source files. Split GGUF files, projectors, and draft heads are retained with their model files. The manifest records file sizes, SHA256 values, checksum evidence, and pinned source identities. SHA256SUMS covers the backed-up source files. Files produced by local conversion retain their conversion records; a pinned base checkpoint does not imply that the converted GGUF was downloaded from that source repository.

• mms-tts-kwf↗

  facebook

• textless_sm_sk_es↗

  facebook