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AI モデル 85003

• GLM-Image↗

  zai-org

  GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency. + Autoregressive generator: a 9B-parameter model initialized from GLM-4-9B-0414, with an expanded vocabulary to incorporate visual tokens. The model first generates a compact encoding of approximately 256 tokens, then expands to 1K–4K tokens, corresponding to 1K–2K high-resolution image outputs. + Diffusion Decoder: a 7B-parameter decoder based on a single-stream DiT architecture for latent-space image decoding. It is equipped with a Glyph Encoder text module, significantly improving accurate text rendering within images. Post-training with decoupled reinforcement learning: the model introduces a fine-grained, modular feedback strategy using the GRPO algorithm, substantially enhancing both semantic understanding and visual detail quality.

• Qwen-Image-2.1-LoRAs↗

  A20labs

• OLMo-2-1124-13B-Instruct-RLVR1↗

  allenai

• magnum-v2-4b-gguf↗

  anthracite-org

• LUMINA↗

  argonne

• DeepFilterNet3-CoreML↗

  aufklarer

• Qwen3.8-27B-Opus-Distill-v2-MTPLX-4bit↗

  barozp

• K2-Horizon-3.7B-GGUF↗

  bartowski

  **Model details:** - Parameter count: 5B (source checkpoint) - Input support: text - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (3.25GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b11462 - if this model's architecture is newly supported, you'll need that release or newer to run them.

• Qwen_Qwen3-8B-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here Run them directly with llama.cpp, or any other llama.cpp based project Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

• WizardLM-2-7B-GGUF↗

  bartowski