add datasets

Former-commit-id: 02e4b47dea1b25905c61f2ace88bab112610f021

README.md

@@ -22,11 +22,11 @@
 
 [23/07/05] Now we support training the **Falcon-7B/40B** models in this repo. Try `--model_name_or_path tiiuae/falcon-7b` and `--lora_target query_key_value` arguments to use the Falcon model.
 
-[23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [HuggingFace Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details.
+[23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [Hugging Face Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details.
 
 [23/06/22] Now we align the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
 
-[23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model. If you want to train with RTX3090, use `git checkout baichuan-7b-rtx3090` to switch to the `baichuan-7b-rtx3090` branch and try the `--baichuan_rtx_gpu true` argument. (Other RTX series GPUs can also be tried)
+[23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model.
 
 [23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized model. (experimental feature)
 
@@ -60,36 +60,36 @@
 ## Provided Datasets
 
 - For pre-training:
-  - [Wiki Demo](data/wiki_demo.txt)
+  - [Wiki Demo (en)](data/wiki_demo.txt)
 - For supervised fine-tuning:
-  - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca)
-  - [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-  - [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
-  - [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
-  - [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
-  - [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
-  - [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
-  - [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
-  - [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
-  - [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
-  - [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
-  - [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
-  - [Web QA (Chinese)](https://huggingface.co/datasets/suolyer/webqa)
-  - [UltraChat](https://github.com/thunlp/UltraChat)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [WebNovel (Chinese)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
-- For reward model training:
-  - [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf)
-  - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1)
-  - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
-  - [GPT-4 Generated Data (Chinese)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
+  - [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [Self-cognition (zh)](data/self_cognition.json)
+  - [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
+  - [RefGPT (zh)](https://github.com/sufengniu/RefGPT)
+  - [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
+  - [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
+  - [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
+  - [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
+  - [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
+  - [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
+  - [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
+  - [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
+  - [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
+  - [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
+  - [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
+  - [UltraChat (en)](https://github.com/thunlp/UltraChat)
+  - [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
+- For reward modelling:
+  - [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
+  - [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
+  - [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
 
 Please refer to [data/README.md](data/README.md) for details.
 
-Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands.
+Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
 
 ```bash
 pip install --upgrade huggingface_hub