diff --git a/docs/mindformers/docs/source_en/guide/supervised_fine_tuning.md b/docs/mindformers/docs/source_en/guide/supervised_fine_tuning.md index 9bc5ccf9b7b71e68b42281bc41eb8d814b0c6aa3..bb330732126ed7a2be5b325e5f4443ad4cc13cdf 100644 --- a/docs/mindformers/docs/source_en/guide/supervised_fine_tuning.md +++ b/docs/mindformers/docs/source_en/guide/supervised_fine_tuning.md @@ -64,7 +64,7 @@ This guide uses [llm-wizard/alpaca-gpt4-data](https://huggingface.co/datasets/ll #### Single-NPU Training -First, prepare the configuration file. This guide provides a fine-tuning configuration file for the Qwen2.5-7B model, `finetune_qwen2_5_7b_8k_1p.yaml`, available for download from the [Gitee repository](https://gitee.com/mindspore/docs/tree/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k_1p.yaml). +First, prepare the configuration file. This guide provides a fine-tuning configuration file for the Qwen2.5-7B model, `finetune_qwen2_5_7b_8k_1p.yaml`, available for download from the [Gitee repository](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k_1p.yaml). > Due to limited single-NPU memory, the `num_layers` in the configuration file is set to 4, used as an example only. @@ -104,7 +104,7 @@ run_mode: Running mode, train: training, finetune: fine-tuning, predict #### Single-Node Training -First, prepare the configuration file. This guide provides a fine-tuning configuration file for the Qwen2.5-7B model, `finetune_qwen2_5_7b_8k.yaml`, available for download from the [Gitee repository](https://gitee.com/mindspore/docs/tree/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k.yaml). +First, prepare the configuration file. This guide provides a fine-tuning configuration file for the Qwen2.5-7B model, `finetune_qwen2_5_7b_8k.yaml`, available for download from the [Gitee repository](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k.yaml). Then, modify the parameters in the configuration file based on actual conditions, mainly including: diff --git a/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron.md b/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron.md index 4fbb4bf550d6822542181f757c18dd6edaa0145b..d2ee7cf99dd4836bf1b0eaf287e04b3463379726 100644 --- a/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron.md +++ b/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron.md @@ -14,7 +14,7 @@ git clone https://github.com/NVIDIA/Megatron-LM.git -b core_r0.12.0 ``` -2. 拷贝[转换脚本](https://gitee.com/mindspore/docs/tree/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron/loader_core_mf.py)到 Megatron-LM/tools/checkpoint/ 目录下。 +2. 拷贝[转换脚本](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/convert_ckpt_to_megatron/convert_ckpt_to_megatron/loader_core_mf.py)到 Megatron-LM/tools/checkpoint/ 目录下。 ## 模型权重准备 diff --git a/docs/mindformers/docs/source_zh_cn/guide/supervised_fine_tuning.md b/docs/mindformers/docs/source_zh_cn/guide/supervised_fine_tuning.md index 6184d32fe2496574e2f84d466100de0ca5bb9559..15ed443a5bfa10853970be7df6cf4898a62c080e 100644 --- a/docs/mindformers/docs/source_zh_cn/guide/supervised_fine_tuning.md +++ b/docs/mindformers/docs/source_zh_cn/guide/supervised_fine_tuning.md @@ -64,7 +64,7 @@ MindSpore Transformers提供在线加载Hugging Face数据集的能力，详细 #### 单卡训练 -首先准备配置文件，本实践流程以Qwen2.5-7B模型为例，提供了一个微调配置文件`finetune_qwen2_5_7b_8k_1p.yaml`，可以在[gitee仓库](https://gitee.com/mindspore/docs/tree/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k_1p.yaml)下载。 +首先准备配置文件，本实践流程以Qwen2.5-7B模型为例，提供了一个微调配置文件`finetune_qwen2_5_7b_8k_1p.yaml`，可以在[gitee仓库](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k_1p.yaml)下载。 > 由于单卡显存有限，配置文件中的`num_layers`被设置为了4，仅作为示例使用。 @@ -106,7 +106,7 @@ run_mode： 运行模式，train：训练，finetune：微调，predict #### 单机训练 -首先准备配置文件，本实践流程以Qwen2.5-7B模型为例，提供了一个微调配置文件`finetune_qwen2_5_7b_8k.yaml`，可以在[gitee仓库](https://gitee.com/mindspore/docs/tree/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k.yaml)下载。 +首先准备配置文件，本实践流程以Qwen2.5-7B模型为例，提供了一个微调配置文件`finetune_qwen2_5_7b_8k.yaml`，可以在[gitee仓库](https://gitee.com/mindspore/docs/blob/r2.7.0rc1/docs/mindformers/docs/source_zh_cn/example/supervised_fine_tuning/finetune_qwen2_5_7b_8k.yaml)下载。 然后根据实际情况修改配置文件中的参数，主要包括： diff --git a/tutorials/source_en/model_infer/ms_infer/llm_inference_overview.md b/tutorials/source_en/model_infer/ms_infer/llm_inference_overview.md index 3d883b9bf66741500d7ebcace3ed491407e53c06..ee8a88d0022c3cf46e8b635ef48baba282d51115 100644 --- a/tutorials/source_en/model_infer/ms_infer/llm_inference_overview.md +++ b/tutorials/source_en/model_infer/ms_infer/llm_inference_overview.md @@ -146,7 +146,7 @@ config = "/path/to/llama2_7b.yaml" model = AutoModel.from_config(config) ``` -In this code, tokenizer.model is a tokenizer file downloaded along with the weights from the Hugging Face official website, containing the token mapping table, while config is the model configuration file from MindFormers, which includes the relevant parameters for running the Llama2 model. You can obtain the sample from [predict_llama2_7b.yaml](https://gitee.com/mindspore/mindformers/blob/r1.5.0/configs/llama2/predict_llama2_7b.yaml) (Note: Change the CKPT weight path to the actual weight path). For details, see [Llama 2](https://gitee.com/mindspore/mindformers/blob/r1.5.0/docs/model_cards/llama2.md#-18). +In this code, tokenizer.model is a tokenizer file downloaded along with the weights from the Hugging Face official website, containing the token mapping table, while config is the model configuration file from MindFormers, which includes the relevant parameters for running the Llama2 model. In addition, if you have special requirements for the model or have a deep understanding of deep learning, you can build your own model. For details, see [Model Development](./model_dev.md). diff --git a/tutorials/source_zh_cn/model_infer/ms_infer/llm_inference_overview.md b/tutorials/source_zh_cn/model_infer/ms_infer/llm_inference_overview.md index 1e14cb04c5cb3515835e6c248fbf7b6c6238a676..30c82f359d1440b681d3338a65ae75ca5d217c48 100644 --- a/tutorials/source_zh_cn/model_infer/ms_infer/llm_inference_overview.md +++ b/tutorials/source_zh_cn/model_infer/ms_infer/llm_inference_overview.md @@ -146,7 +146,7 @@ config = "/path/to/llama2_7b.yaml" model = AutoModel.from_config(config) ``` -其中，tokenizer.model是从Hugging Face官网下载的权重文件中附带的tokenizer文件，里面记录了tokens的映射表；config是MindFormers的模型配置文件，其中包含了Llama2模型运行的相关参数，样例可以在[predict_llama2_7b.yaml](https://gitee.com/mindspore/mindformers/blob/r1.6.0/configs/llama2/predict_llama2_7b.yaml)获取（注意：需要将ckpt权重路径改为实际的权重路径）。更详细的教程可以在[Llama 2](https://gitee.com/mindspore/mindformers/blob/dev/docs/model_cards/llama2.md#-18)获取。 +其中，tokenizer.model是从Hugging Face官网下载的权重文件中附带的tokenizer文件，里面记录了tokens的映射表；config是MindFormers的模型配置文件，其中包含了Llama2模型运行的相关参数。 此外，如果用户对于模型有自己的特殊需求，或者对深度学习有较深认识，也可以选择自己构建模型，详细教程见[从零构建大语言模型推理网络](./model_dev.md)。