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AI 모델 84759

• punk-acestep1.5-v1↗

  ryanontheinside

• mini-glm-moe↗

  samsja

  A small GLM-4 MoE model (543M parameters) for testing and development. Uses the same `Glm4MoeForCausalLM` architecture as the full GLM-4-100B-A10B but with reduced dimensions. This model is designed for testing MoE training pipelines in prime-rl without needing large pretrained checkpoints. It is small enough to run on a single GPU while exercising the same code paths as production models. The architecture mirrors THUDM/GLM-4-100B-A10B: the first layer is a dense MLP, and all subsequent layers use Mixture-of-Experts with a shared expert. Attention uses Grouped Query Attention (GQA) with partial rotary embeddings.

• Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled-GGUF↗

  samuelcardillo

• sarashina2.2-3b↗

  sbintuitions

• f-146↗

  SidXXD

• FLUX.2-klein-4B-onnx↗

  sorge89

• Flux↗

  StableDiffusionVN

• StartLux-Decision-v1-0.8B↗

  startlux-models

• wav2vec2-base-superb-er↗

  superb

  This is a ported version of S3PRL's Wav2Vec2 for the SUPERB Emotion Recognition task. The base model is wav2vec2-base, which is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. For more information refer to SUPERB: Speech processing Universal PERformance Benchmark

• Cobijada_Minimax-H3_Hybrid_Pruned_ComfyUI↗

  taurusduan