221 lines
4.8 KiB
Markdown
221 lines
4.8 KiB
Markdown
我们提供了多样化的大模型微调示例脚本。
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请确保在 `LLaMA-Factory` 目录下执行下述命令。
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## 目录
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- [单 GPU LoRA 微调](#单-gpu-lora-微调)
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- [单 GPU QLoRA 微调](#单-gpu-qlora-微调)
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- [多 GPU LoRA 微调](#多-gpu-lora-微调)
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- [多 GPU 全参数微调](#多-gpu-全参数微调)
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- [合并 LoRA 适配器与模型量化](#合并-lora-适配器与模型量化)
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- [推理 LoRA 模型](#推理-lora-模型)
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- [杂项](#杂项)
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## 示例
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### 单 GPU LoRA 微调
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#### （增量）预训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
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```
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#### 指令监督微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
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```
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#### 奖励模型训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
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```
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#### PPO 训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
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```
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#### DPO 训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
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```
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#### ORPO 训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_orpo.yaml
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```
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#### 多模态指令监督微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
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```
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#### 预处理数据集
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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。
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
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```
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#### 在 MMLU/CMMLU/C-Eval 上评估
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
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```
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#### 批量预测并计算 BLEU 和 ROUGE 分数
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
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```
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### 单 GPU QLoRA 微调
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#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
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```
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#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
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```
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#### 基于 4 比特 AWQ 量化进行指令监督微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
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```
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#### 基于 2 比特 AQLM 量化进行指令监督微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
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```
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### 多 GPU LoRA 微调
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#### 使用 Accelerate 进行单节点训练
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```bash
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bash examples/lora_multi_gpu/single_node.sh
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```
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#### 使用 Accelerate 进行多节点训练
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```bash
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bash examples/lora_multi_gpu/multi_node.sh
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```
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#### 使用 DeepSpeed ZeRO-3 平均分配显存
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```bash
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bash examples/lora_multi_gpu/ds_zero3.sh
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```
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### 多 GPU 全参数微调
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#### 使用 DeepSpeed 进行单节点训练
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```bash
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bash examples/full_multi_gpu/single_node.sh
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```
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#### 使用 DeepSpeed 进行多节点训练
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```bash
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bash examples/full_multi_gpu/multi_node.sh
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```
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#### 批量预测并计算 BLEU 和 ROUGE 分数
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```bash
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bash examples/full_multi_gpu/predict.sh
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```
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### 合并 LoRA 适配器与模型量化
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#### 合并 LoRA 适配器
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注：请勿使用量化后的模型或 `quantization_bit` 参数来合并 LoRA 适配器。
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
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```
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#### 使用 AutoGPTQ 量化模型
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
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```
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### 推理 LoRA 模型
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#### 使用命令行接口
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/merge_lora/llama3_lora_sft.yaml
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```
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#### 使用浏览器界面
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/merge_lora/llama3_lora_sft.yaml
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```
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#### 启动 OpenAI 风格 API
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/merge_lora/llama3_lora_sft.yaml
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```
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### 杂项
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#### 使用 GaLore 进行全参数训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
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```
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#### 使用 BAdam 进行全参数训练
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
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```
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#### LoRA+ 微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
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```
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#### 深度混合微调
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```bash
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
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```
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#### LLaMA-Pro 微调
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```bash
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bash examples/extras/llama_pro/expand.sh
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CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
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```
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#### FSDP+QLoRA 微调
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```bash
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bash examples/extras/fsdp_qlora/single_node.sh
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```
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