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CPM-Ant模型介绍
CPM-Ant是一个开源的中文预训练语言模型,拥有10B参数。它是CPM-Live直播训练过程中的第一个里程碑。训练过程是低成本和环境友好的。基于增量微调(delta tuning)方法,CPM-An
2022-09-16
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突破显存墙,BMInf现已支持GLM-130B
8 月 4 日,清华大学联合智谱 AI 发布了千亿双语大模型 GLM-130B,其在 LAMBADA 数据集上性能超越了 GPT3、OP
2022-09-08
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总结与投票 | 大模型CPM-Ant直播训练的这两个月
总结与展望经过了 68 天的 “自学”,CPM-Ant(CPM-Live 一期模型)终于训练完成。训练过程整体平稳,但也有一些小波折。和现有大模型 BLOOM,OPT 等相比,CPM-Live 系列大
2022-08-15
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BM Architecture Diagram
BM Data BM Data BM Train Open Prompt Delta Center Open Delta BM Train Open Prompt BM Inf BM Inf BM Inf BM Cook BM Cook
BMTrain
The “engine” for big model training. BMTrain performs efficient pre-training and tuning for big models.
Compared with toolkit such as DeepSpeed, BMTrain can save 90% on cost in the training process.
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BMTrain performs amazingly compared to popular frameworks
BMCook
The toolkit for big model “slimming”. BMCook performs efficient compression for big models to improve operating efficiency.
Through the combination of algorithms such as quantization, pruning, distillation, and MoEfication, 90%+ effects of the original model can be maintained, and model inference can be accelerated by 10 times.
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Combination in Any Way
BMInf
Perform big model inference on a thousand-yuan GPU. BMInf performs low-cost and high-efficiency inference for big models,which can perform big model inference with more than 10 billion parameters on a single thousand-yuan GPU (GTX 1060).
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10B Model Decoding Speed
BMInf
PyTorch
OpenPrompt
A “sharp knife” for big model prompt learning. OpenPrompt provides a prompt learning template language with a unified interface. Its compositionality and modularity allow you to easily deploy prompt learning algorithms to run big models.
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Architecture
OpenDelta
Tiny parameters leverage big models. OpenDelta performs parameter-efficient tuning for big models. By only updating very few parameters (less than 5%), the algorithms can achieve the same effect with full-parameter fine-tuning.
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Tool Collaboration
ModelCenter
Big Model Warehouse.ModelCenter implements pre-trained language models (PLMs) based on BMTrain backend. It supports Efficient, Low-Resource, Extendable model usage and distributed training.
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Supported Models
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