From 82132f4e3d393ad2671222ea78fbf2535534a3d8 Mon Sep 17 00:00:00 2001 From: Andrew Date: Wed, 29 Mar 2023 23:18:37 -0700 Subject: [PATCH] [chat] correcting a few obvious typos and grammars errors (#3338) --- applications/Chat/README.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/applications/Chat/README.md b/applications/Chat/README.md index f0abbf039..8f2208495 100644 --- a/applications/Chat/README.md +++ b/applications/Chat/README.md @@ -45,12 +45,12 @@ Coati stands for `ColossalAI Talking Intelligence`. It is the name for the modul The Coati package provides a unified large language model framework that has implemented the following functions - Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms - Supervised datasets collection -- Supervised insturcts fine-tuning +- Supervised instructions fine-tuning - Training reward model - Reinforcement learning with human feedback - Quantization inference - Fast model deploying -- Perfectly integration with the Hugging Face ecosystem, high degree of model customization +- Perfectly integrated with the Hugging Face ecosystem, a high degree of model customization

@@ -98,7 +98,7 @@ pip install . ### Supervised datasets collection -we colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo +we collected 104K bilingual datasets of Chinese and English, and you can find the datasets in this repo [InstructionWild](https://github.com/XueFuzhao/InstructionWild) Here is how we collected the data @@ -188,17 +188,17 @@ if not USE_8BIT: model.eval() ``` -**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. +**Troubleshooting**: if you get errors indicating your CUDA-related libraries are not found when loading the 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct. E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`. #### 4-bit setup -Please ensure you have downloaded HF-format model weights of LLaMA models first. +Please ensure you have downloaded the HF-format model weights of LLaMA models first. -Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script. +Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight conversion scripts. -After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version. +After installing this lib, we may convert the original HF-format LLaMA model weights to a 4-bit version. ```shell CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt @@ -206,7 +206,7 @@ CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`. -**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). +**Troubleshooting**: if you get errors about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo). For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference). @@ -334,7 +334,7 @@ trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer) - [x] implement PPO-ptx fine-tuning - [ ] integrate with Ray - [ ] support more RL paradigms, like Implicit Language Q-Learning (ILQL), -- [ ] support chain of throught by [langchain](https://github.com/hwchase17/langchain) +- [ ] support chain-of-thought by [langchain](https://github.com/hwchase17/langchain) ### Real-time progress You will find our progress in github project broad