diff --git a/README.md b/README.md
index 3d617f47..fc7ff69e 100644
--- a/README.md
+++ b/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)
```
-Example configuration for full-tuning with DeepSpeed ZeRO-2
+Example config.yaml for training with DeepSpeed ZeRO-2
```yaml
compute_environment: LOCAL_MACHINE
@@ -313,6 +315,44 @@ use_cpu: false
Example ds_config.json for training with DeepSpeed ZeRO-2
+
+```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
+ }
+}
+```
+
+使用 DeepSpeed ZeRO-2 进行全参数微调的 DeepSpeed 配置示例
+
+```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
+ }
+}
+```
+
+