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AI 模型 85003

• Qwen3.6-27B-GGUF↗

  ggml-org

  - https://huggingface.co/Qwen/Qwen3.6-27B - https://huggingface.co/z-lab/Qwen3.6-27B-DFlash > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert

• gemma-3-27b-it-qat-q4_0-unquantized↗

  Google

• multiberts-seed_2-step_1100k↗

  Google

• paligemma2-10b-mix-448↗

  Google

• siglip2-large-patch16-512↗

  Google

  SigLIP 2 extends the pretraining objective of SigLIP with prior, independently developed techniques into a unified recipe, for improved semantic understanding, localization, and dense features. You can use the raw model for tasks like zero-shot image classification and image-text retrieval, or as a vision encoder for VLMs (and other vision tasks). 1. Decoder loss 2. Global-local and masked prediction loss 3. Aspect ratio and resolution adaptibility

• t5-efficient-base-el4↗

  Google

• t5gemma-9b-9b-ul2-it↗

  Google

• SmolVLM2-500M-Video-Instruct↗

  HuggingFaceTB

  SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited. - **Developed by:** Hugging Face 🤗 - **Model type:** Multi-modal model (image/multi-image/video/text) - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Architecture:** Based on Idefics3 (see technical summary) - **Demo:** Video Highlight Generator - **Blog:** Blog post

• Huihui-gemma-4-E2B-it-qat-q4_0-unquantized-abliterated-GGUF↗

  huihui-ai

  This is an uncensored version of google/gemma-4-E2B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

• ppo-seals-CartPole-v0↗

  HumanCompatibleAI

  This is a trained model of a **PPO** agent playing **seals/CartPole-v0** using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib