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docs: use swanlab
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README.md
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README.md
@ -54,6 +54,7 @@ Choose your path:
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- [Download from ModelScope Hub](#download-from-modelscope-hub)
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- [Download from Modelers Hub](#download-from-modelers-hub)
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- [Use W&B Logger](#use-wb-logger)
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- [Use SwanLab Logger](#use-swanlab-logger)
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- [Projects using LLaMA Factory](#projects-using-llama-factory)
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- [License](#license)
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- [Citation](#citation)
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@ -66,7 +67,7 @@ Choose your path:
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- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
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- **Advanced algorithms**: [GaLore](https://github.com/jiaweizzhao/GaLore), [BAdam](https://github.com/Ledzy/BAdam), [Adam-mini](https://github.com/zyushun/Adam-mini), DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA and Agent tuning.
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- **Practical tricks**: [FlashAttention-2](https://github.com/Dao-AILab/flash-attention), [Unsloth](https://github.com/unslothai/unsloth), [Liger Kernel](https://github.com/linkedin/Liger-Kernel), RoPE scaling, NEFTune and rsLoRA.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
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- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, SwanLab, etc.
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- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
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## Benchmark
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@ -86,6 +87,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
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## Changelog
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[24/12/21] We supported **[SwanLab](https://github.com/SwanHubX/SwanLab)** experiment tracking and visualization. See [this section](#use-swanlab-logger) for details.
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[24/11/27] We supported fine-tuning the **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** model and the **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** dataset.
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[24/10/09] We supported downloading pre-trained models and datasets from the **[Modelers Hub](https://modelers.cn/models)**. See [this tutorial](#download-from-modelers-hub) for usage.
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@ -633,6 +636,22 @@ run_name: test_run # optional
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Set `WANDB_API_KEY` to [your key](https://wandb.ai/authorize) when launching training tasks to log in with your W&B account.
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### Use SwanLab Logger
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To use [SwanLab](https://github.com/SwanHubX/SwanLab) for logging experimental results, you need to add the following arguments to yaml files.
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```yaml
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use_swanlab: true
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swanlab_project: test_project # optional
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swanlab_experiment_name: test_experiment # optional
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```
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When launching training tasks, you can log in to SwanLab in three ways:
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1. Add `swanlab_api_key=<your_api_key>` to the yaml file, and set it to your [API key](https://swanlab.cn/settings).
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2. Set the environment variable `SWANLAB_API_KEY` to your [API key](https://swanlab.cn/settings).
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3. Use the `swanlab login` command to complete the login.
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## Projects using LLaMA Factory
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If you have a project that should be incorporated, please contact via email or create a pull request.
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README_zh.md
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README_zh.md
@ -55,6 +55,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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- [从魔搭社区下载](#从魔搭社区下载)
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- [从魔乐社区下载](#从魔乐社区下载)
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- [使用 W&B 面板](#使用-wb-面板)
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- [使用 SwanLab 面板](#使用-swanlab-面板)
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- [使用了 LLaMA Factory 的项目](#使用了-llama-factory-的项目)
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- [协议](#协议)
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- [引用](#引用)
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@ -67,7 +68,7 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
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- **先进算法**：[GaLore](https://github.com/jiaweizzhao/GaLore)、[BAdam](https://github.com/Ledzy/BAdam)、[Adam-mini](https://github.com/zyushun/Adam-mini)、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 和 Agent 微调。
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- **实用技巧**：[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)、[Unsloth](https://github.com/unslothai/unsloth)、[Liger Kernel](https://github.com/linkedin/Liger-Kernel)、RoPE scaling、NEFTune 和 rsLoRA。
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
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- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow、SwanLab 等等。
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- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
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## 性能指标
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@ -87,6 +88,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
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## 更新日志
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[24/12/21] 我们支持了 **[SwanLab](https://github.com/SwanHubX/SwanLab)** 跟踪与可视化实验。详细用法请参考 [此部分](#使用-wb-面板)。
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[24/11/27] 我们支持了 **[Skywork-o1](https://huggingface.co/Skywork/Skywork-o1-Open-Llama-3.1-8B)** 模型的微调和 **[OpenO1](https://huggingface.co/datasets/O1-OPEN/OpenO1-SFT)** 数据集。
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[24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。
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@ -634,6 +637,23 @@ run_name: test_run # 可选
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在启动训练任务时，将 `WANDB_API_KEY` 设置为[密钥](https://wandb.ai/authorize)来登录 W&B 账户。
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### 使用 SwanLab 面板
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若要使用 [SwanLab](https://github.com/SwanHubX/SwanLab) 记录实验数据，请在 yaml 文件中添加下面的参数。
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```yaml
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use_swanlab: true
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swanlab_project: test_run # 可选
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swanlab_experiment_name: test_experiment # 可选
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```
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在启动训练任务时，登录SwanLab账户有以下三种方式：
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方式一：在 yaml 文件中添加 `swanlab_api_key=<your_api_key>` ，并设置为你的 [API 密钥](https://swanlab.cn/settings)。
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方式二：将环境变量 `SWANLAB_API_KEY` 设置为你的 [API 密钥](https://swanlab.cn/settings)。
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方式三：启动前使用 `swanlab login` 命令完成登录。
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## 使用了 LLaMA Factory 的项目
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如果您有项目希望添加至下述列表，请通过邮件联系或者创建一个 PR。
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