update readme

README.md

@@ -18,17 +18,17 @@
 
 [23/08/03] Now we support training the **Qwen-7B** model in this repo. Try `--model_name_or_path Qwen/Qwen-7B-Chat` and `--lora_target c_attn` arguments to train the Qwen-7B model. Remember to use `--template chatml` argument when you are using the Qwen-7B-Chat model.
 
-[23/07/31] Now we support dataset streaming. Try `--streaming` and `--max_steps 100` arguments to stream your dataset.
+[23/07/31] Now we support **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
 
 [23/07/29] We release two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/baichuan-13b-sft)) for details.
 
 [23/07/19] Now we support training the **LLaMA-2** models in this repo. Try `--model_name_or_path meta-llama/Llama-2-7b-hf` argument to use the LLaMA-2 model. Remember to use `--template llama2` argument when you are using the LLaMA-2-chat model.
 
-[23/07/18] Now we develop an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
+[23/07/18] Now we develop an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
 
 [23/07/11] Now we support training the **Baichuan-13B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-13B-Base` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--template baichuan` argument when you are using the Baichuan-13B-Chat model.
 
-[23/07/09] Now we release [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
+[23/07/09] Now we release **[FastEdit](https://github.com/hiyouga/FastEdit)**⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
 
 [23/07/07] Now we support training the **InternLM-7B** model in this repo. Try `--model_name_or_path internlm/internlm-7b` argument to use the InternLM model. Remember to use `--template intern` argument when you are using the InternLM-chat model.
 
@@ -72,6 +72,8 @@
 | PPO Training           |                |                   | ✅   | ✅   |
 | DPO Training           | ✅            |                    | ✅   | ✅   |
 
+- Use `--quantization_bit 4/8` argument to enable QLoRA.
+
 ## Provided Datasets
 
 - For pre-training:
@@ -204,8 +206,6 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
     --fp16
 ```
 
-Remember to specify `--lora_target W_pack` if you are using Baichuan models.
-
 ### Reward Model Training
 
 ```bash
@@ -280,12 +280,14 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
 
 ### Distributed Training
 
+#### Use Huggingface Accelerate
+
 ```bash
 accelerate config # configure the environment
 accelerate launch src/train_bash.py # arguments (same as above)
 ```
 
-<details><summary>Example configuration for full-tuning with DeepSpeed ZeRO-2</summary>
+<details><summary>Example config.yaml for training with DeepSpeed ZeRO-2</summary>
 
 ```yaml
 compute_environment: LOCAL_MACHINE
@@ -313,6 +315,44 @@ use_cpu: false
 
 </details>
 
+#### Use DeepSpeed
+
+```bash
+deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
+    --deepspeed ds_config.json \
+    ... # arguments (same as above)
+```
+
+<details><summary>Example ds_config.json for training with DeepSpeed ZeRO-2</summary>
+
+```json
+{
+  "train_micro_batch_size_per_gpu": "auto",
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "zero_allow_untested_optimizer": true,
+  "fp16": {
+    "enabled": "auto",
+    "loss_scale": 0,
+    "initial_scale_power": 16,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },  
+  "zero_optimization": {
+    "stage": 2,
+    "allgather_partitions": true,
+    "allgather_bucket_size": 5e8,
+    "reduce_scatter": true,
+    "reduce_bucket_size": 5e8,
+    "overlap_comm": false,
+    "contiguous_gradients": true
+  }
+}
+```
+
+</details>
+
 ### Evaluation (BLEU and ROUGE_CHINESE)
 
 ```bash