423A35C7/LLaMA-Factory
1
0
Fork 0
mirror of https://github.com/hiyouga/LLaMA-Factory.git synced 2026-01-10 08:00:36 +08:00
Code Issues Packages Projects Releases Wiki Activity

[release] Bye 2025 (#9702)

Browse Source
This commit is contained in:
Yaowei Zheng
2025-12-31 22:22:40 +08:00
committed by GitHub
parent 000526908a
commit 95ac3f2373
59 changed files with 154 additions and 401 deletions
Download Patch File Download Diff File Expand all files Collapse all files

70
examples/README_zh.md
View File

@@ -18,19 +18,19 @@ LLaMA-Factory 默认使用所有可见的计算设备。
基础用法：
```bash
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
```
高级用法：
```bash
CUDA_VISIBLE_DEVICES=0,1 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml \
CUDA_VISIBLE_DEVICES=0,1 llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml \
learning_rate=1e-5 \
logging_steps=1
```
```bash
bash examples/train_lora/llama3_lora_sft.sh
bash examples/train_lora/qwen3_lora_sft.sh
```
## 示例
@@ -40,49 +40,43 @@ bash examples/train_lora/llama3_lora_sft.sh
#### （增量）预训练
```bash
llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_pretrain.yaml
```
#### 指令监督微调
```bash
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
```
#### 多模态指令监督微调
```bash
llamafactory-cli train examples/train_lora/qwen2_5vl_lora_sft.yaml
llamafactory-cli train examples/train_lora/qwen3vl_lora_sft.yaml
```
#### DPO/ORPO/SimPO 训练
```bash
llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_dpo.yaml
```
#### 多模态 DPO/ORPO/SimPO 训练
```bash
llamafactory-cli train examples/train_lora/qwen2_5vl_lora_dpo.yaml
llamafactory-cli train examples/train_lora/qwen3vl_lora_dpo.yaml
```
#### 奖励模型训练
```bash
llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
```
#### PPO 训练
```bash
llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_reward.yaml
```
#### KTO 训练
```bash
llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
llamafactory-cli train examples/train_lora/qwen3_lora_kto.yaml
```
#### 预处理数据集
@@ -90,20 +84,14 @@ llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
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
llamafactory-cli train examples/train_lora/llama3_preprocess.yaml
```
#### 在 MMLU/CMMLU/C-Eval 上评估
```bash
llamafactory-cli eval examples/train_lora/llama3_lora_eval.yaml
llamafactory-cli train examples/train_lora/qwen3_preprocess.yaml
```
#### 多机指令监督微调
```bash
FORCE_TORCHRUN=1 NNODES=2 NODE_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 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/qwen3_lora_sft.yaml
```
### 支持弹性和容错的多机指令监督微调
@@ -111,19 +99,19 @@ FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500
要启动一个支持弹性节点和容错的多机指令微调，在每个节点上执行以下命令。弹性节点数量范围为 `MIN_NNODES:MAX_NNODES`，每个节点最多允许因为错误重启 `MAX_RESTARTS` 次。`RDZV_ID` 应设置为一个唯一的作业 ID（由参与该作业的所有节点共享）。更多新可以参考官方文档 [torchrun](https://docs.pytorch.org/docs/stable/elastic/run.html)。
```bash
FORCE_TORCHRUN=1 MIN_NNODES=1 MAX_NNODES=3 MAX_RESTARTS=3 RDZV_ID=llamafactory MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
FORCE_TORCHRUN=1 MIN_NNODES=1 MAX_NNODES=3 MAX_RESTARTS=3 RDZV_ID=llamafactory MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/qwen3_full_sft.yaml
```
#### 使用 DeepSpeed ZeRO-3 平均分配显存
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/qwen3_lora_sft_ds3.yaml
```
#### 使用 Ray 在 4 张 GPU 上微调
```bash
USE_RAY=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ray.yaml
USE_RAY=1 llamafactory-cli train examples/train_lora/qwen3_lora_sft_ray.yaml
```
### QLoRA 微调
@@ -131,13 +119,13 @@ USE_RAY=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ray.yaml
#### 基于 4/8 比特 Bitsandbytes/HQQ/EETQ 量化进行指令监督微调（推荐）
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
llamafactory-cli train examples/train_qlora/qwen3_lora_sft_otfq.yaml
```
#### 在 NPU 上基于 4 比特 Bitsandbytes 量化进行指令监督微调
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_bnb_npu.yaml
llamafactory-cli train examples/train_qlora/qwen3_lora_sft_bnb_npu.yaml
```
#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
@@ -163,20 +151,20 @@ llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
#### 在单机上进行指令监督微调
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen3_full_sft.yaml
```
#### 在多机上进行指令监督微调
```bash
FORCE_TORCHRUN=1 NNODES=2 NODE_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 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/qwen3_full_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 NODE_RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/qwen3_full_sft.yaml
```
#### 多模态指令监督微调
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2_5vl_full_sft.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen3vl_full_sft.yaml
```
### 合并 LoRA 适配器与模型量化
@@ -186,19 +174,19 @@ FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/qwen2_5vl_full_sft.y
注：请勿使用量化后的模型或 `quantization_bit` 参数来合并 LoRA 适配器。
```bash
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/qwen3_lora_sft.yaml
```
#### 使用 AutoGPTQ 量化模型
```bash
llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
llamafactory-cli export examples/merge_lora/qwen3_gptq.yaml
```
### 保存 Ollama 配置文件
```bash
llamafactory-cli export examples/merge_lora/llama3_full_sft.yaml
llamafactory-cli export examples/merge_lora/qwen3_full_sft.yaml
```
### 推理 LoRA 模型
@@ -206,26 +194,26 @@ llamafactory-cli export examples/merge_lora/llama3_full_sft.yaml
#### 使用 vLLM 多卡推理评估
```
python scripts/vllm_infer.py --model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct --template llama3 --dataset alpaca_en_demo
python scripts/vllm_infer.py --model_name_or_path Qwen/Qwen3-4B-Instruct-2507 --template qwen3_nothink --dataset alpaca_en_demo
python scripts/eval_bleu_rouge.py generated_predictions.jsonl
```
#### 使用命令行对话框
```bash
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
llamafactory-cli chat examples/inference/qwen3_lora_sft.yaml
```
#### 使用浏览器对话框
```bash
llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
llamafactory-cli webchat examples/inference/qwen3_lora_sft.yaml
```
#### 启动 OpenAI 风格 API
```bash
llamafactory-cli api examples/inference/llama3_lora_sft.yaml
llamafactory-cli api examples/inference/qwen3_lora_sft.yaml
```
### 杂项