Update readme (#11141)

README.md

@@ -3,10 +3,10 @@
  
 ---
 
-# 💫 IPEX-LLM
+# 💫 Intel® LLM library for PyTorch*
 **`IPEX-LLM`** is a PyTorch library for running **LLM** on Intel CPU and GPU *(e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max)* with very low latency[^1]. 
 > [!NOTE]
-> - *It is built on top of **Intel Extension for PyTorch** (**`IPEX`**), as well as the excellent work of **`llama.cpp`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
+> - *It runs on top of Intel Extension for PyTorch (**`IPEX`**), and is built on top of the excellent work of **`llama.cpp`**, **`transformers`**, **`bitsandbytes`**, **`vLLM`**, **`qlora`**, **`AutoGPTQ`**, **`AutoAWQ`**, etc.*
 > - *It provides seamless integration with [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html), [ollama](https://ipex-llm.readthedocs.io/en/main/doc/LLM/Quickstart/ollama_quickstart.html), [Text-Generation-WebUI](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/webui_quickstart.html), [HuggingFace transformers](python/llm/example/GPU/HF-Transformers-AutoModels), [HuggingFace PEFT](python/llm/example/GPU/LLM-Finetuning), [LangChain](python/llm/example/GPU/LangChain), [LlamaIndex](python/llm/example/GPU/LlamaIndex), [DeepSpeed-AutoTP](python/llm/example/GPU/Deepspeed-AutoTP), [vLLM](python/llm/example/GPU/vLLM-Serving), [FastChat](python/llm/src/ipex_llm/serving/fastchat), [HuggingFace TRL](python/llm/example/GPU/LLM-Finetuning/DPO), [AutoGen](python/llm/example/CPU/Applications/autogen), [ModeScope](python/llm/example/GPU/ModelScope-Models), etc.* 
 > - ***50+ models** have been optimized/verified on `ipex-llm` (including LLaMA2, Mistral, Mixtral, Gemma, LLaVA, Whisper, ChatGLM, Baichuan, Qwen, RWKV, and more); see the complete list [here](#verified-models).*
 
@@ -48,7 +48,9 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
 </table>
        
 ## Latest Update 🔥 
-- [2024/04] You can now run **Llama 3** on Intel GPU using `llama.cpp` and `ollama`; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama3_llamacpp_ollama_quickstart.html).
+- [2024/05] `ipex-llm` now supports **Axolotl** for LLM finetuning on Intel GPU; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/axolotl_quickstart.html).
+- [2024/04] You can now run **Open WebUI** on Intel GPU using `ipex-llm`; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/open_webui_with_ollama_quickstart.html).
+- [2024/04] You can now run **Llama 3** on Intel GPU using `llama.cpp` and `ollama` with `ipex-llm`; see the quickstart [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama3_llamacpp_ollama_quickstart.html).
 - [2024/04] `ipex-llm` now supports **Llama 3** on both Intel [GPU](python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama3) and [CPU](python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3).
 - [2024/04] `ipex-llm` now provides C++ interface, which can be used as an accelerated backend for running [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html) and [ollama](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/ollama_quickstart.html) on Intel GPU.
 - [2024/03] `bigdl-llm` has now become `ipex-llm` (see the migration guide [here](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/bigdl_llm_migration.html)); you may find the original `BigDL` project [here](https://github.com/intel-analytics/bigdl-2.x).
@@ -80,10 +82,9 @@ See the demo of running [*Text-Generation-WebUI*](https://ipex-llm.readthedocs.i
 
 ### Docker
 - [GPU Inference in C++](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_cpp_xpu_quickstart.html): running `llama.cpp`, `ollama`, `OpenWebUI`, etc., with `ipex-llm` on Intel GPU
-- [GPU Inference in Python](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_pytorch_inference_gpu.html#) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU
+- [GPU Inference in Python](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_pytorch_inference_gpu.html) : running HuggingFace `transformers`, `LangChain`, `LlamaIndex`, `ModelScope`, etc. with `ipex-llm` on Intel GPU
-- [GPU Dev in Visual Studio Code](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/docker_run_pytorch_inference_in_vscode.html): LLM development in python using `ipex-llm` on Intel GPU in VSCode
+- [vLLM on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/vllm_docker_quickstart.html): running `vLLM` serving with `ipex-llm` on Intel GPU 
-- [vLLM on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/fastchat_docker_quickstart.html): serving with `ipex-llm` accelerated `vLLM` on Intel GPU 
+- [FastChat on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/fastchat_docker_quickstart.html): running `FastChat` serving with `ipex-llm` on Intel GPU
-- [FastChat on GPU](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/DockerGuides/fastchat_docker_quickstart.html): serving with `ipex-llm` accelerated `FastChat`on Intel GPU
 
 ### Use
 - [llama.cpp](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/Quickstart/llama_cpp_quickstart.html): running **llama.cpp** (*using C++ interface of `ipex-llm` as an accelerated backend for `llama.cpp`*) on Intel GPU

docs/readthedocs/source/doc/LLM/DockerGuides/fastchat_docker_quickstart.md

@@ -1,6 +1,6 @@
-# Serving using IPEX-LLM integrated FastChat on Intel GPUs via docker
+# FastChat Serving with IPEX-LLM on Intel GPUs via docker
 
-This guide demonstrates how to do LLM serving with `IPEX-LLM` integrated `FastChat` in Docker on Linux with Intel GPUs.
+This guide demonstrates how to run `FastChat` serving with `IPEX-LLM` on Intel GPUs via Docker.
 
 ## Install docker
 

docs/readthedocs/source/doc/LLM/DockerGuides/vllm_cpu_docker_quickstart.md

@@ -1,6 +1,6 @@
-# Serving using IPEX-LLM integrated vLLM on Intel CPU via docker
+# vLLM Serving with IPEX-LLM on Intel CPU via Docker
 
-This guide demonstrates how to do LLM serving with `IPEX-LLM` integrated `vLLM` in Docker on Linux with Intel CPU.
+This guide demonstrates how to run `vLLM` serving with `ipex-llm` on Intel CPU via Docker.
 
 ## Install docker
 

docs/readthedocs/source/doc/LLM/DockerGuides/vllm_docker_quickstart.md

@@ -1,6 +1,6 @@
-# Serving using IPEX-LLM integrated vLLM on Intel GPUs via docker
+# vLLM Serving with IPEX-LLM on Intel GPUs via Docker
 
-This guide demonstrates how to do LLM serving with `IPEX-LLM` integrated `vLLM` in Docker on Linux with Intel GPUs.
+This guide demonstrates how to run `vLLM` serving with `IPEX-LLM` on Intel GPUs via Docker.
 
 ## Install docker
 

docs/readthedocs/source/index.rst

@@ -14,7 +14,7 @@
 ------
 
 ################################################
-💫 IPEX-LLM
+💫 Intel® LLM library for PyTorch*
 ################################################
 
 .. raw:: html
@@ -30,7 +30,7 @@
        <p>
          <ul>
             <li><em>
-               It is built on top of <strong>Intel Extension for PyTorch</strong> (<strong><code><span>IPEX</span></code></strong>), as well as the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc. 
+               It runs on top of Intel Extension for PyTorch (<strong><code><span>IPEX</span></code></strong>), and is built on top of the excellent work of <strong><code><span>llama.cpp</span></code></strong>, <strong><code><span>transfromers</span></code></strong>, <strong><code><span>bitsandbytes</span></code></strong>, <strong><code><span>vLLM</span></code></strong>, <strong><code><span>qlora</span></code></strong>, <strong><code><span>AutoGPTQ</span></code></strong>, <strong><code><span>AutoAWQ</span></code></strong>, etc. 
             </li></em>
             <li><em>
                It provides seamless integration with <a href=doc/LLM/Quickstart/llama_cpp_quickstart.html>llama.cpp</a>, <a href=doc/LLM/Quickstart/ollama_quickstart.html>ollama</a>, <a href=doc/LLM/Quickstart/webui_quickstart.html>Text-Generation-WebUI</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels>HuggingFace transformers</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning>HuggingFace PEFT</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LangChain >LangChain</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LlamaIndex >LlamaIndex</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/Deepspeed-AutoTP >DeepSpeed-AutoTP</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/vLLM-Serving >vLLM</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/src/ipex_llm/serving/fastchat>FastChat</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/LLM-Finetuning/DPO>HuggingFace TRL</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/Applications/autogen >AutoGen</a>, <a href=https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/ModelScope-Models >ModeScope</a>, etc.
@@ -44,6 +44,8 @@
 ************************************************
 Latest update 🔥
 ************************************************
+* [2024/05] ``ipex-llm`` now supports **Axolotl** for LLM finetuning on Intel GPU; see the quickstart `here <doc/LLM/Quickstart/axolotl_quickstart.html>`_.
+* [2024/04] You can now run **Open WebUI** on Intel GPU using ``ipex-llm``; see the quickstart `here <doc/LLM/Quickstart/open_webui_with_ollama_quickstart.html>`_.
 * [2024/04] You can now run **Llama 3** on Intel GPU using ``llama.cpp`` and ``ollama``; see the quickstart `here <doc/LLM/Quickstart/llama3_llamacpp_ollama_quickstart.html>`_.
 * [2024/04] ``ipex-llm`` now supports **Llama 3** on Intel `GPU <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/llama3>`_ and `CPU <https://github.com/intel-analytics/ipex-llm/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/llama3>`_.
 * [2024/04] ``ipex-llm`` now provides C++ interface, which can be used as an accelerated backend for running `llama.cpp <doc/LLM/Quickstart/llama_cpp_quickstart.html>`_ and `ollama <doc/LLM/Quickstart/ollama_quickstart.html>`_ on Intel GPU.
@@ -110,19 +112,16 @@ See the **optimized performance** of ``chatglm2-6b`` and ``llama-2-13b-chat`` mo
 ************************************************
 
 ============================================
-Install ``ipex-llm``
+Docker
 ============================================
 
-* `Windows GPU <doc/LLM/Quickstart/install_windows_gpu.html>`_: installing ``ipex-llm`` on Windows with Intel GPU
+* `GPU Inference in C++ <doc/LLM/DockerGuides/docker_cpp_xpu_quickstart.html>`_: running ``llama.cpp``, ``ollama``, ``OpenWebUI``, etc., with ``ipex-llm`` on Intel GPU
-* `Linux GPU <doc/LLM/Quickstart/install_linux_gpu.html>`_: installing ``ipex-llm`` on Linux with Intel GPU
+* `GPU Inference in Python <doc/LLM/DockerGuides/docker_pytorch_inference_gpu.html>`_: running HuggingFace ``transformers``, ``LangChain``, ``LlamaIndex``, ``ModelScope``, etc. with ``ipex-llm`` on Intel GPU
-* `Docker <https://github.com/intel-analytics/ipex-llm/tree/main/docker/llm>`_: using ``ipex-llm`` dockers on Intel CPU and GPU
+* `vLLM on GPU <doc/LLM/DockerGuides/vllm_docker_quickstart.html>`_: running ``vLLM`` serving with ``ipex-llm`` on Intel GPU 
-
+* `FastChat on GPU <doc/LLM/DockerGuides/fastchat_docker_quickstart.html>`_: running ``FastChat`` serving with ``ipex-llm`` on Intel GPU
-.. seealso::
-
-   For more details, please refer to the `installation guide <doc/LLM/Overview/install.html>`_
 
 ============================================
-Run ``ipex-llm``
+Run
 ============================================
 
 * `llama.cpp <doc/LLM/Quickstart/llama_cpp_quickstart.html>`_: running **llama.cpp** (*using C++ interface of* ``ipex-llm`` *as an accelerated backend for* ``llama.cpp``) on Intel GPU
@@ -133,6 +132,17 @@ Run ``ipex-llm``
 * `Text-Generation-WebUI <doc/LLM/Quickstart/webui_quickstart.html>`_: running ``ipex-llm`` in ``oobabooga`` **WebUI**
 * `Benchmarking <doc/LLM/Quickstart/benchmark_quickstart.html>`_: running  (latency and throughput) benchmarks for ``ipex-llm`` on Intel CPU and GPU
 
+============================================
+Install
+============================================
+
+* `Windows GPU <doc/LLM/Quickstart/install_windows_gpu.html>`_: installing ``ipex-llm`` on Windows with Intel GPU
+* `Linux GPU <doc/LLM/Quickstart/install_linux_gpu.html>`_: installing ``ipex-llm`` on Linux with Intel GPU
+
+.. seealso::
+
+   For more details, please refer to the `installation guide <doc/LLM/Overview/install.html>`_
+
 ============================================
 Code Examples
 ============================================