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LLaMA-Factory-Mirror
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update examples

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hiyouga 2024-06-13 03:26:10 +08:00
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10
examples/README.md
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@ -11,6 +11,8 @@ Make sure to execute these commands in the `LLaMA-Factory` directory.
- [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
- [Extras](#extras)
Use `CUDA_VISIBLE_DEVICES` (GPU) or `ASCEND_RT_VISIBLE_DEVICES` (NPU) to choose computing devices.
## Examples
### LoRA Fine-Tuning
@ -87,7 +89,7 @@ FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llama
#### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
```
### QLoRA Fine-Tuning
@ -121,14 +123,14 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/train_qlora/llama3_lora_s
#### Supervised Fine-Tuning on Single Node
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
```
#### Supervised Fine-Tuning on Multiple Nodes
```bash
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
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_ds3.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_ds3.yaml
```
#### Batch Predicting and Computing BLEU and ROUGE Scores

10
examples/README_zh.md
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@ -11,6 +11,8 @@
- [推理 LoRA 模型](#推理-lora-模型)
- [杂项](#杂项)
使用 `CUDA_VISIBLE_DEVICES`（GPU）或 `ASCEND_RT_VISIBLE_DEVICES`（NPU）选择计算设备。
## 示例
### LoRA 微调
@ -87,7 +89,7 @@ FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llama
#### 使用 DeepSpeed ZeRO-3 平均分配显存
```bash
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
```
### QLoRA 微调
@ -121,14 +123,14 @@ 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/llama3_full_sft_ds3.yaml
```
#### 在多机上进行指令监督微调
```bash
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
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_ds3.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_ds3.yaml
```
#### 批量预测并计算 BLEU 和 ROUGE 分数