pytorch

Introduction A 7B decoder-only LLM with two early exits, trained using EE-LLM with 150B tokens from
330data-juicer
LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LL
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models If you like our project, please give
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models If you like our project, please give
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models If you like our project, please give
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Clone with HTTP git clone https://www.modelscope.cn/LindseyChang/TRLLM-Model.git TRLLM-Model介绍 本模型
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当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。 您可以通过如下git clone命令,或者ModelScope SDK来下载模型 SDK下载 #安装ModelS
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LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LL
380
LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LL
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MoE-LLaVA: Mixture of Experts for Large Vision-Language Models If you like our project, please give
350
当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。 您可以通过如下git clone命令,或者ModelScope SDK来下载模型 SDK下载 #安装ModelS
240
Run Huggingface RWKV5 World Model CPU import torch from transformers import AutoModelForCausalLM, Au
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A bilingual instruction-tuned model of Qwen-7B(https://huggingface.co/Qwen/Qwen-7B) for QAnything(ht
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该模型当前使用的是默认介绍模版,处于“预发布”阶段,页面仅限所有者可见。 请根据模型贡献文档说明,及时完善模型卡片内容。ModelScope平台将在模型卡片完善后展示。谢谢您的理解。 Clone wi
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Introduction A 1.3B decoder-only LLM with two early exits, trained using EE-LLM with 300B tokens fro
280data-juicer
OmniLMM 12B OmniLMM-12B is the most capable version. The model is built based on EVA02-5B and Zephyr
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LLaVA Model Card Model details Model type: LLaVA is an open-source chatbot trained by fine-tuning LL
280
Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in
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MiniCPM MiniCPM 技术报告 Technical Report | OmniLMM 多模态模型 Multi-modal Model | CPM-C 千亿模型试用 ~100B Mode
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Run Huggingface RWKV5 World Model CPU import torch from transformers import AutoModelForCausalLM, Au
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