update examples

Former-commit-id: 19681f93db399d695aa8e35f8ec2a9e720875baa
2024-06-13 03:15:06 +08:00
parent 49b58fd6af
commit 46f441dd37
27 changed files with 128 additions and 199 deletions
--- a/examples/README_zh.md
+++ b/examples/README_zh.md
@@ -4,59 +4,57 @@

 ## 目录

- [单 GPU LoRA 微调](#单-gpu-lora-微调)
- [单 GPU QLoRA 微调](#单-gpu-qlora-微调)
- [多 GPU LoRA 微调](#多-gpu-lora-微调)
- [多 NPU LoRA 微调](#多-npu-lora-微调)
- [多 GPU 全参数微调](#多-gpu-全参数微调)
+- [LoRA 微调](#lora-微调)
+- [QLoRA 微调](#qlora-微调)
+- [全参数微调](#全参数微调)
 - [合并 LoRA 适配器与模型量化](#合并-lora-适配器与模型量化)
 - [推理 LoRA 模型](#推理-lora-模型)
 - [杂项](#杂项)

 ## 示例

-### 单 GPU LoRA 微调
+### LoRA 微调

 #### （增量）预训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml
 ```

 #### 指令监督微调

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```

 #### 多模态指令监督微调

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
+llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
 ```

 #### 奖励模型训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
 ```

 #### PPO 训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
 ```

 #### DPO/ORPO/SimPO 训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
 ```

 #### KTO 训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
 ```

 #### 预处理数据集
@@ -64,95 +62,79 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
 对于大数据集有帮助，在配置中使用 `tokenized_path` 以加载预处理后的数据集。

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
+llamafactory-cli train examples/train_lora/llama3_preprocess.yaml
 ```

 #### 在 MMLU/CMMLU/C-Eval 上评估

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
+llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml
 ```

 #### 批量预测并计算 BLEU 和 ROUGE 分数

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
+llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
 ```

-### 单 GPU QLoRA 微调
-
-#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
+#### 多机指令监督微调

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
-```
-
-#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
-```
-
-#### 基于 4 比特 AWQ 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
-```
-
-#### 基于 2 比特 AQLM 量化进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
-```
-
-### 多 GPU LoRA 微调
-
-#### 在单机上进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
-```
-
-#### 在多机上进行指令监督微调
-
-```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
 ```

 #### 使用 DeepSpeed ZeRO-3 平均分配显存

 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds.yaml
 ```

-### 多 NPU LoRA 微调
+### QLoRA 微调

-#### 使用 DeepSpeed ZeRO-0 进行指令监督微调
+#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）

 ```bash
-ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_bitsandbytes.yaml
 ```

-### 多 GPU 全参数微调
+#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml
+```
+
+#### 基于 4 比特 AWQ 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml
+```
+
+#### 基于 2 比特 AQLM 量化进行指令监督微调
+
+```bash
+llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
+```
+
+### 全参数微调

 #### 在单机上进行指令监督微调

 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 ```

 #### 在多机上进行指令监督微调

 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
-CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
+FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
 ```

 #### 批量预测并计算 BLEU 和 ROUGE 分数

 ```bash
-CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
+llamafactory-cli train examples/train_full/llama3_full_predict.yaml
 ```

 ### 合并 LoRA 适配器与模型量化
@@ -162,35 +144,33 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llam
 注：请勿使用量化后的模型或 `quantization_bit` 参数来合并 LoRA 适配器。

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
+llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
 ```

 #### 使用 AutoGPTQ 量化模型

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
+llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
 ```

 ### 推理 LoRA 模型

-使用 `CUDA_VISIBLE_DEVICES=0,1` 进行多卡推理。
-
 #### 使用命令行接口

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```

 #### 使用浏览器界面

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
+llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
 ```

 #### 启动 OpenAI 风格 API

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
+llamafactory-cli api examples/inference/llama3_lora_sft.yaml
 ```

 ### 杂项
@@ -198,32 +178,32 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.y
 #### 使用 GaLore 进行全参数训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
 ```

 #### 使用 BAdam 进行全参数训练

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
 ```

 #### LoRA+ 微调

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
+llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
 ```

 #### 深度混合微调

 ```bash
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
+llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
 ```

 #### LLaMA-Pro 微调

 ```bash
 bash examples/extras/llama_pro/expand.sh
-CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
+llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
 ```

 #### FSDP+QLoRA 微调