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README.md
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* @Author: LiangSong(sl12160010@gmail.com)
* @Date: 2023-03-10 21:18:35
* @LastEditors: LiangSong(sl12160010@gmail.com)
* @LastEditTime: 2023-04-28 19:49:29
* @LastEditTime: 2023-04-28 19:52:27
* @FilePath: /Open-Llama/README.md
* @Description:
*
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![image4](assets/multiturn_chat.jpeg)
## **更新**
[2023.4.28] Release v2.0
本次更新主要包含以下几个方面，相对于v1版本提升有效训练速度50%，其中pad从30%减少至5%，训练速度从3200token/s提升至3600token/s。0.95 * 3600/(0.7 * 3200)=1.527
**[2023.4.28] Release v2.0**
本次更新主要包含以下几个方面，相对于v1版本提升有效训练速度**50%**，其中pad从**30%**减少至**5%**，训练速度从**3200token/s**提升至**3600token/s**。0.95 * 3600/(0.7 * 3200)=1.527
1. 使用HuggingFace的datasets库进行数据读取，具体流程如下
1. 使用transform函数将不同数据集的数据统一格式为{'text': 'xxx'}
2. 使用Tokenizer进行分词
3. 对长序列进行采样，目前提供三种模式，分别是：截断/采样（参考[Gopher论文](https://arxiv.org/abs/2112.11446)）/切分
4. 可选：对来自不同doc的文本进行拼接。减少了数据中的pad，加速训练；在v1版本中pad占比为30%，使用拼接后pad占比降低为5%。
4. 可选：对来自不同doc的文本进行拼接。减少了数据中的pad，加速训练；在v1版本中pad占比为**30%**，使用拼接后pad占比降低为**5%**。
2. 加入Trainer，对于预训练和指令微调都可以复用，见solver/trainer.py
3. 统一预训练和指令微调训练入口为train_lm.py
4. 提供更方便的配置，可见configs/pretrain_config.yaml

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README_en.md
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* @Author: LiangSong(sl12160010@gmail.com)
* @Date: 2023-03-10 21:18:35
* @LastEditors: LiangSong(sl12160010@gmail.com)
* @LastEditTime: 2023-04-28 19:49:24
* @LastEditTime: 2023-04-28 19:53:01
* @FilePath: /Open-Llama/README_en.md
* @Description:
*
@ -38,15 +38,15 @@ Below is a display of the model's multi-turn dialogue ability regarding code:
## **Updates**
[2023.4.28] Release v2.0
**[2023.4.28] Release v2.0**
This update mainly includes the following aspects, increasing the effective training speed by 50% compared to the v1 version, reducing padding from 30% to 5%, and improving training speed from 3200 tokens/s to 3600 tokens/s. 0.95 * 3600 / (0.7 * 3200) = 1.527
This update mainly includes the following aspects, increasing the effective training speed by **50%** compared to the v1 version, reducing padding from **30%** to **5%**, and improving training speed from **3200 tokens/s** to **3600 tokens/s**. 0.95 * 3600 / (0.7 * 3200) = 1.527
1. Use HuggingFace's datasets library for data reading, with the process as follows:
1. Use the transform function to unify data formats from different datasets to {'text': 'xxx'}
2. Tokenize using Tokenizer
3. Sample long sequences; currently, three modes are provided: truncation, sampling (refer to the [Gopher paper](https://arxiv.org/abs/2112.11446)), and splitting
4. Optional: concatenate texts from different docs, reducing padding in the data and accelerating training. In the v1 version, padding accounted for 30%; after concatenation, padding is reduced to 5%.
4. Optional: concatenate texts from different docs, reducing padding in the data and accelerating training. In the v1 version, padding accounted for **30%**; after concatenation, padding is reduced to **5%**.
2. Add Trainer, which can be reused for both pre-training and instruction fine-tuning, see solver/trainer.py
3. Unify the pre-training and instruction fine-tuning training entry to train_lm.py
4. Provide more convenient configuration, see configs/pretrain_config.yaml