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README.md
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...@@ -78,7 +78,7 @@ pip install mmengine==0.10.3 ...@@ -78,7 +78,7 @@ pip install mmengine==0.10.3
pip install bitsandbytes-0.37.0+das1.0+gitd3d888f.abi0.dtk2404.torch2.1-py3-none-any.whl pip install bitsandbytes-0.37.0+das1.0+gitd3d888f.abi0.dtk2404.torch2.1-py3-none-any.whl
``` ```
2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct)模型； 2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)模型；
3. 修改[llama3_8b_instruct_qlora_alpaca_e3_M.py](./llama3_8b_instruct_qlora_alpaca_e3_M.py)代码中的`pretrained_model_name_or_path`、`data_path`为本地模型、数据地址； 3. 修改[llama3_8b_instruct_qlora_alpaca_e3_M.py](./llama3_8b_instruct_qlora_alpaca_e3_M.py)代码中的`pretrained_model_name_or_path`、`data_path`为本地模型、数据地址；
...@@ -100,7 +100,7 @@ NPROC_PER_NODE=${DCU_NUM} xtuner train ./llama3_8b_instruct_qlora_alpaca_e3_M.py ...@@ -100,7 +100,7 @@ NPROC_PER_NODE=${DCU_NUM} xtuner train ./llama3_8b_instruct_qlora_alpaca_e3_M.py
git clone -b v0.6.3 http://developer.hpccube.com/codes/OpenDAS/llama-factory.git git clone -b v0.6.3 http://developer.hpccube.com/codes/OpenDAS/llama-factory.git
``` ```
2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct)模型； 2. 通过[预训练权重](#预训练权重)下载预训练模型，当前用例使用[Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)模型；
3. `llama3/3.1`训练脚本可参考[这里](./llama-factory/examples/)，特别地，选择`single_node.sh`启动的，需要确认`single_config.yaml`文件中`num_processes`参数与设置的显卡数量一致。 3. `llama3/3.1`训练脚本可参考[这里](./llama-factory/examples/)，特别地，选择`single_node.sh`启动的，需要确认`single_config.yaml`文件中`num_processes`参数与设置的显卡数量一致。
...@@ -257,15 +257,15 @@ python eval.py --model hf --model_args pretrained=/home/llama3/Meta-Llama-3-8B-I ...@@ -257,15 +257,15 @@ python eval.py --model hf --model_args pretrained=/home/llama3/Meta-Llama-3-8B-I
制造,广媒,家居,教育 制造,广媒,家居,教育
## 预训练权重 ## 预训练权重
通过[SCNet AIModels](http://113.200.138.88:18080/aimodels)下载预训练模型: 通过HF下载预训练模型:
- [Meta-Llama-3-8B](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B) - [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- [Meta-Llama-3-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-8B-Instruct) - [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- [Meta-Llama-3-70B](http://113.200.138.88:18080/aimodels/Meta-Llama-3-70B) - [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B)
- [Meta-Llama-3-70B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3-70B-Instruct) - [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)
- [Meta-Llama-3.1-8B](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-8B) - [Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
- [Meta-Llama-3.1-8B-Instruct](http://113.200.138.88:18080/aimodels/Meta-Llama-3.1-8B-Instruct) - [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
- [Meta-Llama-3.1-70B](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-70B) - [Meta-Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B)
- [Meta-Llama-3.1-70B-Instruct](http://113.200.138.88:18080/aimodels/meta-llama/Meta-Llama-3.1-70B-Instruct) - [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
模型目录结构如下： 模型目录结构如下：
```bash ```bash
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