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README.md
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* @Author: LiangSong(sl12160010@gmail.com) * @Author: LiangSong(sl12160010@gmail.com)
* @Date: 2023-03-10 21:18:35 * @Date: 2023-03-10 21:18:35
* @LastEditors: LiangSong(sl12160010@gmail.com) * @LastEditors: LiangSong(sl12160010@gmail.com)
* @LastEditTime: 2023-04-28 22:44:21 * @LastEditTime: 2023-04-29 11:41:10
* @FilePath: /Open-Llama/README.md * @FilePath: /Open-Llama/README.md
* @Description: * @Description:
* *
@ -24,7 +24,7 @@ Open-Llama是一个开源项目，提供了一整套用于构建大型语言模
经过Instruct-tuning的CheckPoint已开源在[HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1)。使用ckpt需要先用下面命令安装最新版本Transformers 经过Instruct-tuning的CheckPoint已开源在[HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1)。使用ckpt需要先用下面命令安装最新版本Transformers
``` python ``` python
pip install git+https://github.com/s-JoL/transformers.git@dev pip install git+https://github.com/huggingface/transformers.git
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
@ -181,7 +181,7 @@ python3 dataset/dataset.py
我们基于Transformers库中的[Llama](https://github.com/facebookresearch/llama)参考论文原文中的2.4 Efficient implementation一节进行了修改， 我们基于Transformers库中的[Llama](https://github.com/facebookresearch/llama)参考论文原文中的2.4 Efficient implementation一节进行了修改，
同时还参考了一些其他论文引入了一些优化。具体来说，我们引入了由META开源的[xformers库](https://github.com/facebookresearch/xformers)中的memory_efficient_attention操作来进行 同时还参考了一些其他论文引入了一些优化。具体来说，我们引入了由META开源的[xformers库](https://github.com/facebookresearch/xformers)中的memory_efficient_attention操作来进行
Self Attention的计算，这对于性能有明显的提升，提升大约30%。 Self Attention的计算，这对于性能有明显的提升，提升大约30%。
具体可以参见[modeling_llama.py](https://github.com/s-JoL/transformers/blob/dev/src/transformers/models/open_llama/modeling_open_llama.py#L230) 具体可以参见[modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/open_llama/modeling_open_llama.py#L229)
同时我们还参考了[Bloom](https://huggingface.co/bigscience/bloom)，对于Token Embedding引入了Stable Embedding以更好的稳定训练。 同时我们还参考了[Bloom](https://huggingface.co/bigscience/bloom)，对于Token Embedding引入了Stable Embedding以更好的稳定训练。

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* @Author: LiangSong(sl12160010@gmail.com) * @Author: LiangSong(sl12160010@gmail.com)
* @Date: 2023-03-10 21:18:35 * @Date: 2023-03-10 21:18:35
* @LastEditors: LiangSong(sl12160010@gmail.com) * @LastEditors: LiangSong(sl12160010@gmail.com)
* @LastEditTime: 2023-04-28 22:44:27 * @LastEditTime: 2023-04-29 11:41:20
* @FilePath: /Open-Llama/README_en.md * @FilePath: /Open-Llama/README_en.md
* @Description: * @Description:
* *
@ -24,7 +24,7 @@ Open-Llama is an open-source project that offers a complete training pipeline fo
The CheckPoint after Instruct-tuning is open-source on [HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1). To use the CheckPoint, first, install the latest version of Transformers with the following command: The CheckPoint after Instruct-tuning is open-source on [HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1). To use the CheckPoint, first, install the latest version of Transformers with the following command:
``` python ``` python
pip install git+https://github.com/s-JoL/transformers.git@dev pip install git+https://github.com/huggingface/transformers.git
from transformers import AutoModelForCausalLM, AutoTokenizer from transformers import AutoModelForCausalLM, AutoTokenizer
@ -190,7 +190,7 @@ python3 dataset/dataset.py
### Model Structure ### Model Structure
We modified according to the section 2.4 Efficient implementation of the [Llama](https://github.com/facebookresearch/llama) paper in the Transformers library, and also referenced other papers to introduce some optimizations. Specifically, we used the memory_efficient_attention operation from the [xformers library](https://github.com/facebookresearch/xformers) open-sourced by META for Self Attention computation, which has a significant performance improvement of approximately 30%. Further details can be found in [modeling_llama.py](https://github.com/s-JoL/transformers/blob/dev/src/transformers/models/open_llama/modeling_open_llama.py#L230). We modified according to the section 2.4 Efficient implementation of the [Llama](https://github.com/facebookresearch/llama) paper in the Transformers library, and also referenced other papers to introduce some optimizations. Specifically, we used the memory_efficient_attention operation from the [xformers library](https://github.com/facebookresearch/xformers) open-sourced by META for Self Attention computation, which has a significant performance improvement of approximately 30%. Further details can be found in [modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/open_llama/modeling_open_llama.py#L229).
Additionally, we referred to [Bloom](https://huggingface.co/bigscience/bloom) and introduced Stable Embedding for Token Embedding to better stabilize training. Additionally, we referred to [Bloom](https://huggingface.co/bigscience/bloom) and introduced Stable Embedding for Token Embedding to better stabilize training.