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				|  | @ -32,6 +32,9 @@ ChatGLM2-6B 开源模型旨在与开源社区一起推动大模型技术发展 | |||
| * [fastllm](https://github.com/ztxz16/fastllm/): 全平台加速推理方案,单GPU批量推理每秒可达10000+token,手机端最低3G内存实时运行(骁龙865上约4~5 token/s) | ||||
| * [chatglm.cpp](https://github.com/li-plus/chatglm.cpp): 类似 llama.cpp 的 CPU 量化加速推理方案,实现 Mac 笔记本上实时对话 | ||||
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 | ||||
| 支持 ChatGLM-6B 和相关应用在线训练的示例项目: | ||||
| * [ChatGLM2-6B 的部署与微调教程](https://www.heywhale.com/mw/project/64984a7b72ebe240516ae79c) | ||||
| 
 | ||||
| ## 评测结果 | ||||
| 我们选取了部分中英文典型数据集进行了评测,以下为 ChatGLM2-6B 模型在 [MMLU](https://github.com/hendrycks/test) (英文)、[C-Eval](https://cevalbenchmark.com/static/leaderboard.html)(中文)、[GSM8K](https://github.com/openai/grade-school-math)(数学)、[BBH](https://github.com/suzgunmirac/BIG-Bench-Hard)(英文) 上的测评结果。在 [evaluation](./evaluation/README.md) 中提供了在 C-Eval 上进行测评的脚本。 | ||||
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 | ||||
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|  | @ -24,6 +24,9 @@ Although the model strives to ensure the compliance and accuracy of data at each | |||
| Open source projects that accelerate ChatGLM2: | ||||
| * [chatglm.cpp](https://github.com/li-plus/chatglm.cpp): Real-time CPU inference on a MacBook accelerated by quantization, similar to llama.cpp. | ||||
| 
 | ||||
| Example projects supporting online training of ChatGLM-6B and related applications: | ||||
| * [ChatGLM-6B deployment and fine-tuning tutorial](https://www.heywhale.com/mw/project/64984a7b72ebe240516ae79c) | ||||
| 
 | ||||
| ## Evaluation | ||||
| We selected some typical Chinese and English datasets for evaluation. Below are the evaluation results of the ChatGLM2-6B model on [MMLU](https://github.com/hendrycks/test) (English), [C-Eval](https://cevalbenchmark.com/static/leaderboard.html) (Chinese), [GSM8K](https://github.com/openai/grade-school-math) (Mathematics), [BBH](https://github.com/suzgunmirac/BIG-Bench-Hard) (English). | ||||
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	 Zhengxiao Du
						Zhengxiao Du