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AI 모델 84759

• ecaresnet50t.a1_in1k↗

  timm

  This model features: * ReLU activations * tiered 3-layer stem of 3x3 convolutions with pooling * 2x2 average pool + 1x1 convolution shortcut downsample * Efficient Channel Attention Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup

• mobilevitv2_175.cvnets_in1k↗

  timm

• embeddinggemma-2-GGUF↗

  unsloth

  Unsloth Dynamic 3.0 achieves superior accuracy & outperforms other leading quants. **EmbeddingGemma 2** is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering.

• Wan2.2-Animate-14B-Diffusers↗

  Wan-AI

• llama-68m↗

  Xenova

• Qwen3.6-35B-A3B-Abliterated-Heretic-GGUF↗

  Youssofal

  By applying Heretic on the Qwen 3.6 sparse-MoE text stack, the base refusal behavior was removed at the weight level. The result keeps Qwen3.6-35B-A3B's multimodal architecture and general capability profile, while no longer defaulting to the original refusal pattern. Qwen3.6-35B-A3B is a 35.95B sparse MoE vision-language model with roughly 3B active parameters per token, 40 text layers, 256 routed experts, and 8 active experts per token. This release was produced with a Heretic MPOA/SOMA-style sibling-transfer run, finalized with a split-MoE input-side intervention on the accepted candidate.

• Qwen3.8-27B-Uncensored-YMQ-MTP-GGUF↗

  zerodigest

  ⚡ Fuel the Lab: Keep the Optimization Loops Running Every single ZeroDigest YMQ-MTP release is handcrafted and manually calibrated via intensive importance-matrix sweeps to protect critical logic pathways. This project is entirely independent research—no automation bots, no corporate backers, and no external funding. Running multi-hour compute arrays consumes massive local infrastructure overhead out-of-pocket. Consider checking out our compiler or supporting our compute costs! 👉 Developer Resources & Support Paths: 🛠️ View Compiler Source on GitHub (YMQ v2.0) 🚀 Deploy weights on RunPod Cloud Compute (Affiliate Link) ☕ Support the Lab on Ko-fi (One-Time / Monthly) This repository features advanced, custom architecture-aware quantizations of **Qwen3.8-27B (Uncensored)** processed directly from official raw `BF16` source files using the custom **YMQ-Compiler (v2.0)** log-space framework.

• distilbert-portuguese-cased↗

  adalbertojunior

• Agente-GPT-Qwen2.5-3B-Instruct-Spanish_8bit↗

  Agnuxo

• gemma-2-2b-jpn-it-gguf↗

  alfredplpl