模型我们在MTBench-101上评估了21个流行的LLMs,包括2个闭源LLMs(即GPT-3.5/GPT-4(OpenAI,2023))和19个开源LLMs(即Llama2-Chat(7B,13B)(Touvron等人,2023),Mistral-Instruct(7B,8x7B,DPO)(Jiang等人,2023a),Qwen-Chat(7B,14B)(Bai等人,2023),Yi-Chat(6B,34B)(Yi,2023),ChatGLM2-6B/ChatGLM3-6B(D...
MTBench101是一个专门设计用于评估大型语言模型在多轮对话中的精细能力的测试。以下是关于MTBench101的详细解答：目的：填补空白：MTBench101旨在填补以往基准测试在多轮对话能力评估上的空白，特别是那些忽视了真实对话复杂性和细微差别的测试。全面评估：通过提供一个全面的基准测试，来准确评估LLMs在多轮对...
为此，我们引入了MT-Bench101，一个专门设计用于评估LLMs在多轮对话中的精细能力的测试。通过对手头真实多轮对话数据的详细分析，我们构建了一个包含4208个轮次、1388个多轮对话和13个不同任务的三级分层能力分类体系。我们利用21个流行的LLMs进行全面评估，发现不同模型在不同任务中的对话轮次表现存在差异...
mt-bench-101 Public [ACL 2024] MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues 52 23 2 contributions in the last year Contribution Graph Day of Week December Dec January Jan February Feb March Mar April Apr May May June Jun July Ju...
opencompass/datasets/subjective/mtbench101.py| docs/zh_cn/advanced_guides/compassbench_intro.md ) repos: 62 changes: 62 additions & 0 deletions 62 configs/datasets/subjective/multiround/mtbench101_judge.py Original file line numberDiff line numberDiff line change @@ -0,0 +1,62 @@ from ...
我的目标：就要100或者101 我要上电视
x 随着卡牌的不断推出，越来越多的卡牌扎堆在购买万能碎片产出，玩家的道具早已消耗殆尽，对此，你有什么...
在开启Memory Extension Mode后,选择Benchmark档位。使用Aida64进行内存测试,效能如下:读取119GB/s,写入115GB/s,复制112GB/s,延迟54.3ns。读取带宽提升6%,写入带宽提升21%,复制带宽提升13%,延迟提升10%。左图仅开启XMP;右图开启XMP、开启Memory Extension Mode 档位Benchmark 3.超频测试 MSI Z790MPOWER,BIOS版本:...
1This benchmark is not used for the average calculation Add one or more devices and compare In the following list you can select (and also search for) devices that should be added to the comparison. You can select more than one device. ...
Benchmarks 3DMark - 3DMark Sling Shot Extreme (ES 3.1) Unlimited Physics 100% Dimensity 8100 + min: 4716 avg: 4867 median: 4866.5 (46%) max: 5017 Points 83% SD 8 Gen 1 + min: 2283 avg: 3965 median: 4021 (38%) max: 4771 Points 77% MT8188J + 3747 Points (35%) ...