数据来源：https://www.datalearner.com/ai-models/llm-evaluation?modelSize=7b 上图是按照MMLU排序，...
ModelsizeModeldescription UltraOurmostcapablemodelthatdeliversstate-of-the-artperformanceacrossawide rangeofhighlycomplextasks,includingreasoningandmultimodaltasks.Itis efficientlyserveableatscaleonTPUacceleratorsduetotheGeminiarchitecture. ProAperformance-optimizedmodelintermsofcostaswellaslatencythatdelivers significantperf...
数据来源:https://www.datalearner.com/ai-models/llm-evaluation?modelSize=7b上图是按照MMLU排序,并且都是70亿参数规模的结果。可以看到Gemma2 7B是MMLU得分最高的。这个分数与马斯克的Grok差不多,也接近Qwen-14B的水平。就变成评测HumanEval来说,Gemma 7B与CodeLlama 7B差不多。
今年7月,Google DeepMind就展示过一个叫RT-2(Robotic Transformer 2)的机器人,它将电脑中训练的「视觉-语言模型」(visual-language model,VLM)与机器人在物理世界中的动作关联起来,形成「视觉-语言-动作模型」(visual-language-action,VLA),即RT-2。相较于过去为特定任务编写指令的机器人,RT-2能够与人通过自然语...
今年7月,Google DeepMind就展示过一个叫RT-2(Robotic Transformer 2)的机器人,它将电脑中训练的「视觉-语言模型」(visual-language model,VLM)与机器人在物理世界中的动作关联起来,形成「视觉-语言-动作模型」(visual-language-action,VLA),即RT-2。相较于过去为特定任务编写指令的机器人,RT-2能够与人通过自然语...
谷歌在几个小时前发布了Gemini大模型,号称历史最强的大模型。这是一系列的多模态的大模型,在各项评分中超过了GPT-4V,可能是目前最强的模型。 Gemini总体简介 Gemini-Ultra Gemini-Pro Gemini-Nano Gemini的技术细节 Gemini的评测结果 Gemini的后续使用 Gemini总体简介 ...
Gemini Nano 1.0:The Gemini Nano model size is designed to run on smartphones, initially launched on theGoogle Pixel 8. It's built to perform on-device tasks that require efficient AI processing without connecting to external servers, such as suggesting replies within chat applications, understandin...
The Nano model is targeted at on-device use cases. There are two different versions of Gemini Nano: The Nano-1 model has 1.8 billion parameters, while Nano-2 has 3.25 billion parameters. Among the places where Nano is being embedded is the Google Pixel 9 smartphone. ...
设置训练超参数 training_args = { 'learning_rate': 1e-5, 'batch_size': 32, 'num_epochs': 10, 'device': 'cuda' # 使用GPU训练 } # 开始训练模型 gemini_model.train(train_data, val_data, **training_args) 模型评估 训练完成后,需要对模型进行评估,以确定其在不同任务上的表现。评估通常包括...
num_batches = len(dataframe) // batch_size for i in range(num_batches): # 计算当前批次的开始索引 start_idx = i * batch_size # 计算当前批次的结束索引 end_idx = start_idx + batch_size # 获取当前批次的数据 batch_data = dataframe[start_idx:end_idx] ...