Best Practice with LLaMA-Factory (OpenBMB#711)

* add llamafactory examples

* tiny fix

* update doc about inference

README.md

@@ -131,8 +131,8 @@ Advancing popular visual capabilites from MiniCPM-V series, MiniCPM-o 2.6 can pr
   In addition to its friendly size, MiniCPM-o 2.6 also shows **state-of-the-art token density** (i.e., number of pixels encoded into each visual token). **It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models**. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-o 2.6 can efficiently support **multimodal live streaming** on end-side devices such as iPad.
 
 -  💫  **Easy Usage.**
-MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
 
+MiniCPM-o 2.6 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-o-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-o-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), and (6) online web demo on [server](https://minicpm-omni-webdemo-us.modelbest.cn/).
 
 **Model Architecture.**
 
@@ -2488,7 +2488,7 @@ We support simple fine-tuning with Hugging Face for MiniCPM-o 2.6, MiniCPM-V 2.6
 
 We support fine-tuning MiniCPM-o-2.6 and MiniCPM-V 2.6 with the LLaMA-Factory framework. LLaMA-Factory provides a solution for flexibly customizing the fine-tuning (Lora/Full/Qlora) of 200+ LLMs without the need for coding through the built-in web UI LLaMABoard. It supports various training methods like sft/ppo/dpo/kto and advanced algorithms like Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA.
 
-Best Practices: [MiniCPM-V-2.6 | MiniCPM-o-2.6](./docs/llamafactory_train.md). 
+Best Practices: [MiniCPM-o-2.6 | MiniCPM-V-2.6](./docs/llamafactory_train_and_infer.md). 
 
 
 ### With the SWIFT Framework <!-- omit in toc -->
@@ -2574,4 +2574,4 @@ If you find our model/code/paper helpful, please consider citing our papers 📝
   journal={arXiv preprint arXiv:2408.01800},
   year={2024}
 }
-```
+```

README_zh.md

@@ -121,8 +121,8 @@ MiniCPM-o 2.6 进一步优化了 MiniCPM-V 2.6 的众多视觉理解能力，其
 
 
 - 💫 **易于使用。**
-MiniCPM-o 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#基于-llamacppollamavllm-的高效推理) 支持高吞吐量和内存高效的推理，(4) 通过[LLaMA-Factory](./docs/llamafactory_train.md)框架针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 部署于服务器的在线 [demo](https://minicpm-omni-webdemo-us.modelbest.cn/)。
 
+MiniCPM-o 2.6 可以通过多种方式轻松使用：(1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-omni/examples/llava/README-minicpmo2.6.md) 支持在本地设备上进行高效的 CPU 推理，(2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) 和 [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) 格式的量化模型，有 16 种尺寸，(3) [vLLM](#基于-llamacppollamavllm-的高效推理) 支持高吞吐量和内存高效的推理，(4) 通过[LLaMA-Factory](./docs/llamafactory_train_and_infer.md)框架针对新领域和任务进行微调，(5) 使用 [Gradio](#本地-webui-demo-) 快速设置本地 WebUI 演示，(6) 部署于服务器的在线 [demo](https://minicpm-omni-webdemo-us.modelbest.cn/)。
 
 **模型架构。**
 
@@ -2498,7 +2498,7 @@ ollama 用法请参考[我们的fork ollama](https://github.com/OpenBMB/ollama/b
 
 我们支持使用 LLaMA-Factory 微调 MiniCPM-o-2.6 和 MiniCPM-V 2.6。LLaMA-Factory 提供了一种灵活定制 200 多个大型语言模型（LLM）微调（Lora/Full/Qlora）解决方案，无需编写代码，通过内置的 Web 用户界面 LLaMABoard 即可实现训练/推理/评估。它支持多种训练方法，如 sft/ppo/dpo/kto，并且还支持如 Galore/BAdam/LLaMA-Pro/Pissa/LongLoRA 等高级算法。
 
-最佳实践: [MiniCPM-V-2.6 | MiniCPM-o-2.6](https://github.com/openbmb/MiniCPM-V/blob/main/docs/llamafactory_train.md). 
+最佳实践: [MiniCPM-o-2.6 | MiniCPM-V-2.6](./docs/llamafactory_train_and_infer.md). 
 
 
 ### 使用 SWIFT 框架 <!-- omit in toc -->
@@ -2586,4 +2586,4 @@ ollama 用法请参考[我们的fork ollama](https://github.com/OpenBMB/ollama/b
   journal={arXiv preprint arXiv:2408.01800},
   year={2024}
 }
-```
+```