> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-zh/xun-lian.md).

# 训练

- [概览](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/training.md)
- [Jupyter 机器学习训练](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/jupyter-ml-training.md): 在 Clore.ai 上搭建支持 GPU 的 JupyterLab，用于机器学习训练
- [DreamBooth](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/dreambooth.md): 在 Clore.ai 的 GPU 上使用 DreamBooth 训练自定义图像模型
- [Kohya Training](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/kohya-training.md): 在 Clore.ai 上使用 Kohya 为 Stable Diffusion 训练 LoRA 和 DreamBooth
- [微调 LLM](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/finetune-llm.md): 在 Clore.ai 的 GPU 上使用高效技术微调自定义 LLM
- [Unsloth 2 倍更快微调](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/unsloth-finetune.md): 在 Clore.ai 上使用 Unsloth，以少 70% VRAM 实现 LLM 微调速度提升 2 倍
- [Axolotl 通用微调](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/axolotl-training.md): 在 Clore.ai 上使用 Axolotl 进行 YAML 驱动的 LLM 微调——LoRA、QLoRA、DPO、多 GPU
- [DeepSpeed 训练](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/deepspeed-training.md): 在 Clore.ai 的 GPU 上使用 DeepSpeed 高效训练大型模型
- [HuggingFace Transformers](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/huggingface-transformers.md): 在 Clore.ai 上将 HuggingFace Transformers 用于 NLP、视觉和音频
- [LLaMA-Factory](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/llama-factory.md): 使用 LLaMA-Factory 在 Clore.ai 的 GPU 上，通过 LoRA/QLoRA 和网页界面微调 100+ 个 LLM
- [LitGPT](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/litgpt.md)
- [Mergekit 模型合并](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/mergekit.md)
- [TRL（RLHF/DPO 训练）](https://docs.clore.ai/guides/guides_v2-zh/xun-lian/trl.md)


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