Update Readme (#8959)

README.md

@@ -7,7 +7,8 @@
 ---
 ## BigDL-LLM
 
-**[`bigdl-llm`](python/llm)** is a library for running ***LLM*** (large language model) on your Intel ***laptop*** or ***GPU*** using INT4 with very low latency[^1] (for any **PyTorch** model).
+**[`bigdl-llm`](python/llm)** is a library for running **LLM** (large language model) on Intel **XPU** (from *Laptop* to *GPU* to *Cloud*) using **INT4** with very low latency[^1] (for any **PyTorch** model).
+
 > *It is built on top of the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [gptq](https://github.com/IST-DASLab/gptq), [ggml](https://github.com/ggerganov/ggml), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
 
 ### Latest update
@@ -25,15 +26,19 @@ See the ***optimized performance*** of `chatglm2-6b`, `llama-2-13b-chat`, and `s
 
 ### `bigdl-llm` quickstart
 
-#### Install
+- [CPU INT4](#cpu-int4)
-You may install **`bigdl-llm`** as follows:
+- [GPU INT4](#gpu-int4)
+- [More Low-Bit Support](#more-low-bit-support)
+
+#### CPU INT4
+##### Install
+You may install **`bigdl-llm`** on Intel CPU as follows:
 ```bash
 pip install --pre --upgrade bigdl-llm[all]
 ```
 > Note: `bigdl-llm` has been tested on Python 3.9
 
-#### Run Model
+##### Run Model
-
 You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
 
 ```python
@@ -41,7 +46,7 @@ You may apply INT4 optimizations to any Hugging Face *Transformers* models as fo
 from bigdl.llm.transformers import AutoModelForCausalLM
 model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
 
-#run the optimized model
+#run the optimized model on CPU
 from transformers import AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 input_ids = tokenizer.encode(input_str, ...)
@@ -50,23 +55,55 @@ output = tokenizer.batch_decode(output_ids)
 ```
 *See the complete examples [here](python/llm/example/transformers/transformers_int4/).*
 
->**Note**: You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows: 
+#### GPU INT4
-  >```python
+##### Install
-  >model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
+You may install **`bigdl-llm`** on Intel GPU as follows:
-  >```
+```bash
-  >*See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
+# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+# you can install specific ipex/torch version for your need
+pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+```
+> Note: `bigdl-llm` has been tested on Python 3.9
 
-
+##### Run Model
-After the model is optimizaed using INT4 (or INT8/INT5), you may also save and load the optimized model as follows:
+You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
 
 ```python
-model.save_low_bit(model_path)
+#load Hugging Face Transformers model with INT4 optimizations
+from bigdl.llm.transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
 
+#run the optimized model on Intel GPU
+model = model.to('xpu')
+
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+input_ids = tokenizer.encode(input_str, ...).to('xpu')
+output_ids = model.generate(input_ids, ...)
+output = tokenizer.batch_decode(output_ids.cpu())
+```
+*See the complete examples [here](python/llm/example/transformers/transformers_int4/).*
+
+#### More Low-Bit Support
+##### Save and load
+
+After the model is optimized using `bigdl-llm`, you may save and load the model as follows:
+```python
+model.save_low_bit(model_path)
 new_model = AutoModelForCausalLM.load_low_bit(model_path)
 ```
 *See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
 
-***For more details, please refer to the `bigdl-llm` [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) and [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
+##### Additonal data types
+ 
+In addition to INT4, You may apply other low bit optimizations (such as *INT8*, *INT5*, *NF4*, etc.) as follows: 
+```python
+model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int8")
+```
+*See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
+
+
+***For more details, please refer to the `bigdl-llm` [Document](https://test-bigdl-llm.readthedocs.io/en/main/doc/LLM/index.html), [Readme](python/llm), [Tutorial](https://github.com/intel-analytics/bigdl-llm-tutorial) and [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).***
 
 ---
 ## Overview of the complete BigDL project

docs/readthedocs/source/index.rst

@@ -1,37 +1,37 @@
 .. meta::
    :google-site-verification: S66K6GAclKw1RroxU0Rka_2d1LZFVe27M0gRneEsIVI
 
-=================================================
+################################################
 The BigDL Project
-=================================================
+################################################
 
 ------
 
----------------------------------
+************************************************
 BigDL-LLM: low-Bit LLM library
----------------------------------
+************************************************
 
 .. raw:: html
 
    <p>
-      <a href="https://github.com/intel-analytics/BigDL/tree/main/python/llm"><code><span>bigdl-llm</span></code></a> is a library for running <strong>LLM</strong> (large language model) on your Intel <strong>laptop</strong> or <strong>GPU</strong> using INT4 with very low latency <sup><a href="#footnote-perf" id="ref-perf">[1]</a></sup> (for any <strong>PyTorch</strong> model).
+      <a href="https://github.com/intel-analytics/BigDL/tree/main/python/llm"><code><span>bigdl-llm</span></code></a> is a library for running <strong>LLM</strong> (large language model) on Intel <strong>XPU</strong> (from <em>Laptop</em> to <em>GPU</em> to <em>Cloud</em>) using <strong>INT4</strong> with very low latency <sup><a href="#footnote-perf" id="ref-perf">[1]</a></sup> (for any <strong>PyTorch</strong> model).
    </p>
 
 .. note::
 
    It is built on top of the excellent work of `llama.cpp <https://github.com/ggerganov/llama.cpp>`_, `gptq <https://github.com/IST-DASLab/gptq>`_, `bitsandbytes <https://github.com/TimDettmers/bitsandbytes>`_, `qlora <https://github.com/artidoro/qlora>`_, etc.
 
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 Latest update
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 - ``bigdl-llm`` now supports Intel Arc and Flex GPU; see the the latest GPU examples `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/gpu>`_.
 - ``bigdl-llm`` tutorial is released `here <https://github.com/intel-analytics/bigdl-llm-tutorial>`_.
 - Over 20 models have been verified on ``bigdl-llm``, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, MPT, Falcon, Dolly-v1/Dolly-v2, StarCoder, Whisper, QWen, Baichuan,* and more; see the complete list `here <https://github.com/intel-analytics/BigDL/tree/main/python/llm/README.md#verified-models>`_.
 
 
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 ``bigdl-llm`` demos
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 
 See the **optimized performance** of ``chatglm2-6b``, ``llama-2-13b-chat``, and ``starcoder-15.5b`` models on a 12th Gen Intel Core CPU below.
 
@@ -42,11 +42,18 @@ See the **optimized performance** of ``chatglm2-6b``, ``llama-2-13b-chat``, and
             <img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-models3.png" width='76%'>
   </p>
 
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 ``bigdl-llm`` quickstart
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+============================================
 
-You may install ``bigdl-llm`` as follows:
+- `CPU <#cpu-quickstart>`_
+- `GPU <#gpu-quickstart>`_
+
+--------------------------------------------
+CPU Quickstart
+--------------------------------------------
+
+You may install ``bigdl-llm`` on Intel CPU as follows as follows:
 
 .. code-block:: console
 
@@ -64,20 +71,53 @@ You can then apply INT4 optimizations to any Hugging Face *Transformers* models
    from bigdl.llm.transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
 
-   #run the optimized model
+   #run the optimized model on Intel CPU
    from transformers import AutoTokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_path)
    input_ids = tokenizer.encode(input_str, ...)
    output_ids = model.generate(input_ids, ...)
    output = tokenizer.batch_decode(output_ids)
 
-**For more details, please refer to the bigdl-llm** `Readme <https://github.com/intel-analytics/BigDL/tree/main/python/llm>`_, `Tutorial <https://github.com/intel-analytics/bigdl-llm-tutorial>`_ and `API Doc <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html>`_.
+--------------------------------------------
+GPU Quickstart
+--------------------------------------------
+
+You may install ``bigdl-llm`` on Intel GPU as follows as follows:
+
+.. code-block:: console
+
+   # below command will install intel_extension_for_pytorch==2.0.110+xpu as default
+   # you can install specific ipex/torch version for your need
+   pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
+
+.. note::
+
+   ``bigdl-llm`` has been tested on Python 3.9.
+
+You can then apply INT4 optimizations to any Hugging Face *Transformers* models on Intel GPU as follows.
+
+.. code-block:: python
+
+   #load Hugging Face Transformers model with INT4 optimizations
+   from bigdl.llm.transformers import AutoModelForCausalLM
+   model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
+
+   #run the optimized model on Intel GPU
+   model = model.to('xpu')
+
+   from transformers import AutoTokenizer
+   tokenizer = AutoTokenizer.from_pretrained(model_path)
+   input_ids = tokenizer.encode(input_str, ...).to('xpu')
+   output_ids = model.generate(input_ids, ...)
+   output = tokenizer.batch_decode(output_ids.cpu())
+
+**For more details, please refer to the bigdl-llm** `Document <doc/LLM/index.html>`_, `Readme <https://github.com/intel-analytics/BigDL/tree/main/python/llm>`_, `Tutorial <https://github.com/intel-analytics/bigdl-llm-tutorial>`_ and `API Doc <doc/PythonAPI/LLM/index.html>`_.
 
 ------
 
----------------------------------
+************************************************
 Overview of the complete BigDL project
----------------------------------
+************************************************
 `BigDL <https://github.com/intel-analytics/bigdl>`_ seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
 
 - `LLM <https://github.com/intel-analytics/BigDL/tree/main/python/llm>`_: Low-bit (INT3/INT4/INT5/INT8) large language model library for Intel CPU/GPU
@@ -90,9 +130,9 @@ Overview of the complete BigDL project
 
 ------
 
----------------------------------
+************************************************
 Choosing the right BigDL library
----------------------------------
+************************************************
 
 .. graphviz::