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AI 모델 183
- gemma-scope-2b-pt-res↗
Google
- plip↗
vinid
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
- Qwen-Image-2.1-PE-I2I↗
阿里巴巴 · Qwen
🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord We are excited to open-source **Qwen-Image-2.1**, a unified text-to-image generation and image editing model in the Qwen family. With just **7B parameters in its visual generation component** (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. Image editing **prompt rewriting model** for Qwen-Image-2.1. A fine-tuned Qwen3.5-VL 9B that takes a vague editing instruction plus input image(s) and produces a precise, actionable prompt suitable for downstream image editing.
- Llama-3.2-3B-Instruct-FP8-dynamic↗
RedHatAI
- **Model Architecture:** Meta-Llama-3.2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to Llama-3.2-3B-Instruct, this models is intended for assistant-like chat. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 9/25/2024 - **Version:** 1.0 - **License(s):** llama3.2 - **Model Developers:** Neural Magic Quantized version of Llama-3.2-3B-Instruct. It achieves an average score of 50.88 on a subset of task from the OpenLLM benchmark (version 1), whereas the unquantized model achieves 51.70. This model was obtained by quantizing the weights and activations of Llama-3.2-3B-Instruct to FP8 data type, ready for inference with vLLM built from source. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
- gemma-scope-2-12b-pt↗
Google
- Wan2.1-I2V-14B-720P↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper (Coming soon)    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features: - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. This repository contains our I2V-14B model, which is capable of generating 720P high-definition videos. After thousands of rounds of human evaluations, this model has outperformed both closed-source and open-source alternatives, achieving state-of-the-art performance.
- gemma-scope-2-270m-pt↗
Google
- gemma-scope-2-4b-it↗
Google
- Qwen-14B-Chat↗
阿里巴巴 · Qwen
🤗 Hugging Face    |   🤖 ModelScope    |    📑 Paper    ｜   🖥️ Demo **通义千问-14B（Qwen-14B）**是阿里云研发的通义千问大模型系列的140亿参数规模的模型。Qwen-14B是基于Transformer的大语言模型, 在超大规模的预训练数据上进行训练得到。预训练数据类型多样，覆盖广泛，包括大量网络文本、专业书籍、代码等。同时，在Qwen-14B的基础上，我们使用对齐机制打造了基于大语言模型的AI助手Qwen-14B-Chat。本仓库为Qwen-14B-Chat的仓库。 **Qwen-14B** is the 14B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-14B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-14B, we release Qwen-14B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. This repository is the one for Qwen-14B-Chat.
- stack-edu-classifier-javascript↗
HuggingFaceTB
This is a classifier for scoring the educational value of code files in The Stack v2 dataset, it is a finetuned version of bigcode/starencoder with a classification head on code files annotated by Llama3.1-70B-Instruct. We use this classifier for building Stack-Edu dataset used for training SmolLM2, see paper. Each classifier is trained on one programming language. While the classifier performs well in distinguishing high-quality code in its target language (JS in this case), there are some limitations: - Scope: The model's performance might change for other datasets, in particular for out of distribution samples. The classifier's context is 1024 tokens, which might not be sufficient to assess the quality of some long code files. - Bias: The model's performance is dependent on the quality and representativeness of the training data and the LLM used for the annotation. Biases in both can affect the classifier's judgments. It might overfit to thoroughly commented code. - Context: The classifier evaluates individual code files without considering broader context, which might impact its effectiveness in certain scenarios.