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AI 模型 84760

• Qwen3.6-27B-Abliterated-Heretic-Uncensored-MLX-4bit↗

  Youssofal

• RMBG-2.0↗

  1038lab

• Ternary-Bonsai-2-27B-Uncensored-Heretic-v2-mlx-2bit-compact↗

  3hs4n

• Nanbeige4.3-33B-GSQ-RCO-GGUF↗

  abenzerps

  Mixed-precision **GSQ-RCO** GGUF quantizations of Nanbeige/Nanbeige4.3-33B, optimized for efficient, high-fidelity local inference in `llama.cpp`. **64k Long-Context (Low-VRAM)** Using 4-bit KV cache (`-ctk q4_0 -ctv q4_0`) allows 65k context to run within **< 12 GB total VRAM** on consumer 12 GB GPUs (e.g. RTX 3060 12GB, RTX 4070):

• phi-2-sft-alpaca_gpt4_en-ep1-GGUF↗

  afrideva

• MiniMax-H3-Character-Swap-LoRA↗

  akatz-ai

  **V1.5 is the recommended checkpoint for new character swaps.** It replaces a selected person in a source performance with a character from an image reference. - **Download V1.5** — recommended, strength 1.0. - **Download V1** — retained for existing workflows and reproducible comparisons. Install H3 Relay 2.0 and follow the workflow guide for models, runtime and input paths.

• Ovis2-4B↗

  ATH-MaaS

  We are pleased to announce the release of **Ovis2**, our latest advancement in multi-modal large language models (MLLMs). Ovis2 inherits the innovative architectural design of the Ovis series, aimed at structurally aligning visual and textual embeddings. As the successor to Ovis1.6, Ovis2 incorporates significant improvements in both dataset curation and training methodologies. We use VLMEvalKit, as employed in the OpenCompass multimodal and reasoning leaderboard, to evaluate Ovis2. Below is a code snippet demonstrating how to run Ovis with various input types. For additional usage instructions, including inference wrapper and Gradio UI, please refer to Ovis GitHub.

• Qwen3.8-Distilled-4B-NPU2↗

  Atomic-Germ

• Laguna-XS-2.1-MLX-8bit↗

  AtomicChat

• ERNIE-Image-Aes↗

  baidu