Update Readme (#8959)
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Jason Dai
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README.md
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README.md
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@ -7,7 +7,8 @@
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---
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## BigDL-LLM
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**[`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).
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**[`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).
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> *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.*
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### Latest update
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@ -25,15 +26,19 @@ See the ***optimized performance*** of `chatglm2-6b`, `llama-2-13b-chat`, and `s
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### `bigdl-llm` quickstart
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#### Install
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You may install **`bigdl-llm`** as follows:
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- [CPU INT4](#cpu-int4)
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- [GPU INT4](#gpu-int4)
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- [More Low-Bit Support](#more-low-bit-support)
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#### CPU INT4
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##### Install
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You may install **`bigdl-llm`** on Intel CPU as follows:
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```bash
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pip install --pre --upgrade bigdl-llm[all]
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```
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> Note: `bigdl-llm` has been tested on Python 3.9
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#### Run Model
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##### Run Model
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You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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```python
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@ -41,7 +46,7 @@ You may apply INT4 optimizations to any Hugging Face *Transformers* models as fo
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model
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#run the optimized model on CPU
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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@ -50,23 +55,55 @@ output = tokenizer.batch_decode(output_ids)
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```
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*See the complete examples [here](python/llm/example/transformers/transformers_int4/).*
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>**Note**: You may apply more low bit optimizations (including INT8, INT5 and INT4) as follows:
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>```python
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>model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int5")
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>```
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>*See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
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#### GPU INT4
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##### Install
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You may install **`bigdl-llm`** on Intel GPU as follows:
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```bash
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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```
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> Note: `bigdl-llm` has been tested on Python 3.9
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After the model is optimizaed using INT4 (or INT8/INT5), you may also save and load the optimized model as follows:
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##### Run Model
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You may apply INT4 optimizations to any Hugging Face *Transformers* models as follows.
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```python
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model.save_low_bit(model_path)
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#load Hugging Face Transformers model with INT4 optimizations
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel GPU
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model = model.to('xpu')
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...).to('xpu')
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids.cpu())
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```
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*See the complete examples [here](python/llm/example/transformers/transformers_int4/).*
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#### More Low-Bit Support
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##### Save and load
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After the model is optimized using `bigdl-llm`, you may save and load the model as follows:
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```python
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model.save_low_bit(model_path)
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new_model = AutoModelForCausalLM.load_low_bit(model_path)
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```
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*See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
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***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).***
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##### Additonal data types
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In addition to INT4, You may apply other low bit optimizations (such as *INT8*, *INT5*, *NF4*, etc.) as follows:
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```python
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit="sym_int8")
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```
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*See the complete example [here](python/llm/example/transformers/transformers_low_bit/).*
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***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).***
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---
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## Overview of the complete BigDL project
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@ -1,37 +1,37 @@
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.. meta::
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:google-site-verification: S66K6GAclKw1RroxU0Rka_2d1LZFVe27M0gRneEsIVI
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=================================================
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################################################
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The BigDL Project
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=================================================
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################################################
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------
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---------------------------------
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************************************************
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BigDL-LLM: low-Bit LLM library
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---------------------------------
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************************************************
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.. raw:: html
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<p>
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<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).
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<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).
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</p>
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.. note::
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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.
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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Latest update
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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- ``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>`_.
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- ``bigdl-llm`` tutorial is released `here <https://github.com/intel-analytics/bigdl-llm-tutorial>`_.
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- 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>`_.
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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``bigdl-llm`` demos
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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See the **optimized performance** of ``chatglm2-6b``, ``llama-2-13b-chat``, and ``starcoder-15.5b`` models on a 12th Gen Intel Core CPU below.
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@ -42,11 +42,18 @@ See the **optimized performance** of ``chatglm2-6b``, ``llama-2-13b-chat``, and
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<img src="https://llm-assets.readthedocs.io/en/latest/_images/llm-models3.png" width='76%'>
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</p>
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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``bigdl-llm`` quickstart
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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============================================
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You may install ``bigdl-llm`` as follows:
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- `CPU <#cpu-quickstart>`_
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- `GPU <#gpu-quickstart>`_
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--------------------------------------------
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CPU Quickstart
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--------------------------------------------
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You may install ``bigdl-llm`` on Intel CPU as follows as follows:
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.. code-block:: console
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@ -64,20 +71,53 @@ You can then apply INT4 optimizations to any Hugging Face *Transformers* models
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model
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#run the optimized model on Intel CPU
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...)
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids)
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**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>`_.
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--------------------------------------------
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GPU Quickstart
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--------------------------------------------
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You may install ``bigdl-llm`` on Intel GPU as follows as follows:
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.. code-block:: console
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# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
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# you can install specific ipex/torch version for your need
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pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
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.. note::
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``bigdl-llm`` has been tested on Python 3.9.
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You can then apply INT4 optimizations to any Hugging Face *Transformers* models on Intel GPU as follows.
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.. code-block:: python
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#load Hugging Face Transformers model with INT4 optimizations
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from bigdl.llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
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#run the optimized model on Intel GPU
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model = model.to('xpu')
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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input_ids = tokenizer.encode(input_str, ...).to('xpu')
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output_ids = model.generate(input_ids, ...)
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output = tokenizer.batch_decode(output_ids.cpu())
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**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>`_.
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------
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---------------------------------
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************************************************
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Overview of the complete BigDL project
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---------------------------------
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************************************************
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`BigDL <https://github.com/intel-analytics/bigdl>`_ seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:
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- `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
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@ -90,9 +130,9 @@ Overview of the complete BigDL project
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------
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---------------------------------
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************************************************
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Choosing the right BigDL library
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---------------------------------
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************************************************
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.. graphviz::
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