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AI 模型 85003

• hindi_gpt2↗

  NebulaByte

• nomic-embed-text-v1-unsupervised↗

  nomic-ai

• Qwen-Image-Edit-NVPCB-OVSL2SL↗

  NVIDIA

• Mapperatorinator-v32-mini↗

  OliBomby

• clip-vit-base-patch16↗

  OpenAI

  Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. The base model uses a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.

• InternVL2_5-4B↗

  OpenGVLab

  We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.

• Octans-Qwen3-UI-Code-4B-GGUF↗

  prithivMLmods

• LTX-2.3-GGUF↗

  QuantStack

  This GGUF file is a direct conversion of Lightricks/LTX-2.3 Type | Name | Location | Download | ------------ | -------------------------------------------------- | --------------------------------- | ------------------------- | Main Model | LTX-2.3 | `ComfyUI/models/unet` | GGUF (this repo) | Text Encoder | Gemma 3 | `ComfyUI/models/text_encoders` | Safetensors / GGUF | | Text Encoder | Text Projection | `ComfyUI/models/text_encoders` | Safetensors| | VAE Video / VAE Audio | vae | `ComfyUI/models/vae` | Safetensors | Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.

• Qwen3 30B A3B 2507 (Reasoning)↗

  阿里巴巴 · Qwen

• Qwen3-Next-80B-A3B-Instruct-GGUF↗

  阿里巴巴 · Qwen

  Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models **Qwen3-Next**. **Qwen3-Next-80B-A3B** is the first installment in the Qwen3-Next series and features the following key enchancements: - **Hybrid Attention**: Replaces standard attention with the combination of **Gated DeltaNet** and **Gated Attention**, enabling efficient context modeling for ultra-long context length. - **High-Sparsity Mixture-of-Experts (MoE)**: Achieves an extreme low activation ratio in MoE layers, drastically reducing FLOPs per token while preserving model capacity. - **Stability Optimizations**: Includes techniques such as **zero-centered and weight-decayed layernorm**, and other stabilizing enhancements for robust pre-training and post-training. - **Multi-Token Prediction (MTP)**: Boosts pretraining model performance and accelerates inference. We are seeing strong performance in terms of both parameter efficiency and inference speed for Qwen3-Next-80B-A3B: - Qwen3-Next-80B-A3B-Base outperforms Qwen3-32B-Base on downstream tasks with 10% of the total training cost and with 10 times inference throughput for context over 32K tokens. - Qwen3-Next-80B-A3B-Instruct performs on par with Qwen3-235B-A22B-Instruct-2507 on certain benchmarks, while demonstrating significant advantages in handling ultra-long-context tasks up to 256K tokens.