Add mddocs index (#11411)

docs/mddocs/Overview/KeyFeatures/langchain_api.md

@@ -1,44 +0,0 @@
-# LangChain API
-
-You may run the models using the LangChain API in `ipex-llm`.
-
-## Using Hugging Face `transformers` INT4 Format
-
-You may run any Hugging Face *Transformers* model (with INT4 optimiztions applied) using the LangChain API as follows:
-
-```python
-from ipex_llm.langchain.llms import TransformersLLM
-from ipex_llm.langchain.embeddings import TransformersEmbeddings
-from langchain.chains.question_answering import load_qa_chain
-
-embeddings = TransformersEmbeddings.from_model_id(model_id=model_path)
-ipex_llm = TransformersLLM.from_model_id(model_id=model_path, ...)
-
-doc_chain = load_qa_chain(ipex_llm, ...)
-output = doc_chain.run(...)
-```
-
-> [!TIP]
-> See the examples [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/LangChain)
-
-## Using Native INT4 Format
-
-You may also convert Hugging Face *Transformers* models into native INT4 format, and then run the converted models using the LangChain API as follows.
-
-> [!NOTE]
-> - Currently only llama/bloom/gptneox/starcoder model families are supported; for other models, you may use the Hugging Face ``transformers`` INT4 format as described [above](./langchain_api.md#using-hugging-face-transformers-int4-format).
-> - You may choose the corresponding API developed for specific native models to load the converted model.
-
-```python
-from ipex_llm.langchain.llms import LlamaLLM
-from ipex_llm.langchain.embeddings import LlamaEmbeddings
-from langchain.chains.question_answering import load_qa_chain
-
-# switch to GptneoxEmbeddings/BloomEmbeddings/StarcoderEmbeddings to load other models
-embeddings = LlamaEmbeddings(model_path='/path/to/converted/model.bin')
-# switch to GptneoxLLM/BloomLLM/StarcoderLLM to load other models
-ipex_llm = LlamaLLM(model_path='/path/to/converted/model.bin')
-
-doc_chain = load_qa_chain(ipex_llm, ...)
-doc_chain.run(...)
-```

docs/mddocs/Overview/KeyFeatures/native_format.md

@@ -1,29 +0,0 @@
-# Native Format
-
-You may also convert Hugging Face *Transformers* models into native INT4 format for maximum performance as follows.
-
-> [!NOTE]
-> Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; you may use the corresponding API to load the converted model. (For other models, you can use the Hugging Face ``transformers`` format as described [here](./hugging_face_format.md))
-
-
-```python
-# convert the model
-from ipex_llm import llm_convert
-ipex_llm_path = llm_convert(model='/path/to/model/',
-                            outfile='/path/to/output/',
-                            outtype='int4',
-                            model_family="llama")
-
-# load the converted model
-# switch to ChatGLMForCausalLM/GptneoxForCausalLM/BloomForCausalLM/StarcoderForCausalLM to load other models
-from ipex_llm.transformers import LlamaForCausalLM
-llm = LlamaForCausalLM.from_pretrained("/path/to/output/model.bin", native=True, ...)
-
-# run the converted model
-input_ids = llm.tokenize(prompt)
-output_ids = llm.generate(input_ids, ...)
-output = llm.batch_decode(output_ids)
-```
-
-> [!NOTE] 
-> See the complete example [here](https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/Native-Models)

docs/mddocs/Overview/KeyFeatures/transformers_style_api.md

@@ -1,6 +0,0 @@
-# `transformers`-style API
-
-You may run the LLMs using `transformers`-style API in `ipex-llm`.
-
-* [Hugging Face `transformers` Format](./hugging_face_format.md)
-* [Native Format](./native_format.md)

docs/mddocs/README.md

@@ -0,0 +1,49 @@
+# IPEX-LLM Documentation
+
+## Table of Contents
+
+- [LLM in 5 minutes](./Overview/llm.md)
+- [Installation](./Overview/install.md)
+  - [CPU](./Overview/install_cpu.md)
+  - [GPU](./Overview/install_gpu.md)
+- [Docker Guides](./DockerGuides/)
+  - [Overview of IPEX-LLM Containers for Intel GPU](./DockerGuides/docker_windows_gpu.md)
+  - [Python Inference using IPEX-LLM on Intel GPU](./DockerGuides/docker_pytorch_inference_gpu.md)
+  - [Run/Develop PyTorch in VSCode with Docker on Intel GPU](./DockerGuides/docker_run_pytorch_inference_in_vscode.md)
+  - [Run llama.cpp/Ollama/Open-WebUI on an Intel GPU via Docker](./DockerGuides/docker_cpp_xpu_quickstart.md)
+  - [FastChat Serving with IPEX-LLM on Intel GPUs via docker](./DockerGuides/fastchat_docker_quickstart.md)
+  - [vLLM Serving with IPEX-LLM on Intel GPUs via Docker](./DockerGuides/vllm_docker_quickstart.md)
+  - [vLLM Serving with IPEX-LLM on Intel CPU via Docker](./DockerGuides/vllm_cpu_docker_quickstart.md)
+- [Quickstart](https://github.com/intel-analytics/ipex-llm/tree/main/docs/mddocs/Quickstart/)
+  - [`bigdl-llm` Migration Guide](./Quickstart/bigdl_llm_migration.md)
+  - [Install IPEX-LLM on Linux with Intel GPU](./Quickstart/install_linux_gpu.md)
+  - [Install IPEX-LLM on Windows with Intel GPU](./Quickstart/install_windows_gpu.md)
+  - [Run Local RAG using Langchain-Chatchat on Intel CPU and GPU](./Quickstart/chatchat_quickstart.md)
+  - [Run Text Generation WebUI on Intel GPU](./Quickstart/webui_quickstart.md)
+  - [Run Open WebUI with Intel GPU](./Quickstart/open_webui_with_ollama_quickstart.md)
+  - [Run PrivateGPT with IPEX-LLM on Intel GPU](./Quickstart/privateGPT_quickstart.md)
+  - [Run Coding Copilot in VSCode with Intel GPU](./Quickstart/continue_quickstart.md)
+  - [Run Dify on Intel GPU](./Quickstart/dify_quickstart.md)
+  - [Run Performance Benchmarking with IPEX-LLM](./Quickstart/benchmark_quickstart.md)
+  - [Run llama.cpp with IPEX-LLM on Intel GPU](./Quickstart/llama_cpp_quickstart.md)
+  - [Run Ollama with IPEX-LLM on Intel GPU](./Quickstart/ollama_quickstart.md)
+  - [Run Llama 3 on Intel GPU using llama.cpp and ollama with IPEX-LLM](./Quickstart/llama3_llamacpp_ollama_quickstart.md)
+  - [Serving using IPEX-LLM and FastChat](./Quickstart/fastchat_quickstart.md)
+  - [Serving using IPEX-LLM and vLLM on Intel GPU](./Quickstart/vLLM_quickstart.md)
+  - [Finetune LLM with Axolotl on Intel GPU](./Quickstart/axolotl_quickstart.md)
+  - [Run IPEX-LLM serving on Multiple Intel GPUs using DeepSpeed AutoTP and FastApi](./Quickstart/deepspeed_autotp_fastapi_quickstart.md)
+  - [Run RAGFlow with IPEX-LLM on Intel GPU](./Quickstart/ragflow_quickstart.md)
+- [Key Features](./Overview/KeyFeatures/)
+  - [PyTorch API](./Overview/KeyFeatures/optimize_model.md)
+  - [`transformers`-style API](./Overview/KeyFeatures/hugging_face_format.md)
+  - [GPU Supports](./Overview/KeyFeatures/gpu_supports.md)
+    - [Inference on GPU](./Overview/KeyFeatures/inference_on_gpu.md)
+    - [Finetune (QLoRA)](./Overview/KeyFeatures/finetune.md)
+    - [Multi Intel GPUs selection](./Overview/KeyFeatures/multi_gpus_selection.md)
+- [Examples](../../python/llm/example/)
+  - [CPU](../../python/llm/example/CPU/)
+  - [GPU](../../python/llm/example/GPU/)
+- [API Reference](./PythonAPI/)
+  - [IPEX-LLM PyTorch API](./PythonAPI/optimize.md)
+  - [IPEX-LLM `transformers`-style API](./PythonAPI/transformers.md)
+- [FQA](./Overview/FAQ/faq.md)