From 1100c3b47a54b707feb010d7f8babd0933a31a6c Mon Sep 17 00:00:00 2001 From: Jintao Huang Date: Fri, 11 Apr 2025 14:06:26 +0800 Subject: [PATCH] fix docs --- "docs/source/GetStarted/SWIFT\345\256\211\350\243\205.md" | 2 -- ...\256\255\347\273\203\344\270\216\345\276\256\350\260\203.md" | 2 +- docs/source_en/GetStarted/SWIFT-installation.md | 2 -- docs/source_en/Instruction/Pre-training-and-Fine-tuning.md | 2 +- 4 files changed, 2 insertions(+), 6 deletions(-) diff --git "a/docs/source/GetStarted/SWIFT\345\256\211\350\243\205.md" "b/docs/source/GetStarted/SWIFT\345\256\211\350\243\205.md" index 14b867854e..82229bf6a3 100644 --- "a/docs/source/GetStarted/SWIFT\345\256\211\350\243\205.md" +++ "b/docs/source/GetStarted/SWIFT\345\256\211\350\243\205.md" @@ -8,8 +8,6 @@ pip install 'ms-swift' # 使用评测 pip install 'ms-swift[eval]' -U -# 使用序列并行 -pip install 'ms-swift[seq_parallel]' -U # 全能力 pip install 'ms-swift[all]' -U ``` diff --git "a/docs/source/Instruction/\351\242\204\350\256\255\347\273\203\344\270\216\345\276\256\350\260\203.md" "b/docs/source/Instruction/\351\242\204\350\256\255\347\273\203\344\270\216\345\276\256\350\260\203.md" index 004928f068..19fe24953e 100644 --- "a/docs/source/Instruction/\351\242\204\350\256\255\347\273\203\344\270\216\345\276\256\350\260\203.md" +++ "b/docs/source/Instruction/\351\242\204\350\256\255\347\273\203\344\270\216\345\276\256\350\260\203.md" @@ -65,8 +65,8 @@ ms-swift使用了分层式的设计思想，用户可以使用命令行界面、 - Any-to-Any模型训练：参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/all_to_all)。 - 其他能力： - 数据流式读取: 在数据量较大时减少内存使用。参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/streaming/train.sh)。 - - 序列并行: 参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/sequence_parallel)。 - packing: 将多个序列拼成一个，让每个训练样本尽可能接近max_length，提高显卡利用率，参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/packing/train.sh)。 + - 长文本训练: 参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/long_text)。 - lazy tokenize: 在训练期间对数据进行tokenize而不是在训练前tokenize（多模态模型可以避免在训练前读入所有多模态资源），这可以避免预处理等待并节约内存。参考[这里](https://github.com/modelscope/swift/blob/main/examples/train/lazy_tokenize/train.sh)。 小帖士： diff --git a/docs/source_en/GetStarted/SWIFT-installation.md b/docs/source_en/GetStarted/SWIFT-installation.md index fd69f2c0e0..4cf428e976 100644 --- a/docs/source_en/GetStarted/SWIFT-installation.md +++ b/docs/source_en/GetStarted/SWIFT-installation.md @@ -8,8 +8,6 @@ You can install it using pip: pip install 'ms-swift' # For evaluation usage pip install 'ms-swift[eval]' -U -# For sequence parallel usage -pip install 'ms-swift[seq_parallel]' -U # Full capabilities pip install 'ms-swift[all]' -U ``` diff --git a/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md b/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md index 6aa8d31eb2..126d534e7f 100644 --- a/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md +++ b/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md @@ -68,8 +68,8 @@ Additionally, we offer a series of scripts to help you understand the training c - Any-to-Any Model Training: Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/all_to_all). - Other Capabilities: - Streaming Data Reading: Reduces memory usage when handling large datasets. Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/streaming/train.sh). - - Sequence Parallelism: Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/sequence_parallel). - Packing: Combines multiple sequences into one, making each training sample as close to max_length as possible to improve GPU utilization. Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/packing/train.sh). + - Long Text Training: Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/long_text). - Lazy Tokenize: Performs tokenization during training instead of pre-training (for multi-modal models, this avoids the need to load all multi-modal resources before training), which can reduce preprocessing wait times and save memory. Refer to [here](https://github.com/modelscope/swift/blob/main/examples/train/lazy_tokenize/train.sh).