From c450c85489330853b033a693f3dc356baff4e46c Mon Sep 17 00:00:00 2001
From: Jason Dai <jason.dai@gmail.com>
Date: Wed, 27 Mar 2024 20:06:40 +0800
Subject: [PATCH] Delete llm/readme.md (#10569)

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 python/llm/README.md              | 305 ------------------------------
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 delete mode 100644 python/llm/README.md

diff --git a/docs/readthedocs/source/index.rst b/docs/readthedocs/source/index.rst
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--- a/docs/readthedocs/source/index.rst
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@@ -30,7 +30,7 @@
        <p>
          <ul>
             <li><em>
-               It is built on top of <strong>Intel Extension for PyTorch</strong> (<strong>IPEX</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 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. 
             </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/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 tansformers</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.
diff --git a/python/llm/README.md b/python/llm/README.md
deleted file mode 100644
index dd46a945faf..00000000000
--- a/python/llm/README.md
+++ /dev/null
@@ -1,305 +0,0 @@
-## IPEX-LLM
-**[`ipex-llm`](https://ipex-llm.readthedocs.io/en/latest/doc/LLM/index.html)** 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.*
-    
-### Demos
-See the ***optimized performance*** of `chatglm2-6b` and `llama-2-13b-chat` models on 12th Gen Intel Core CPU and Intel Arc GPU below.
-
-<table width="100%">
-  <tr>
-    <td align="center" colspan="2">12th Gen Intel Core CPU</td>
-    <td align="center" colspan="2">Intel Arc GPU</td>
-  </tr>
-  <tr>
-    <td>
-      <a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-6b.gif" ></a>
-    </td>
-    <td>
-      <a href="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama-2-13b-chat.gif"></a>
-    </td>
-    <td>
-      <a href="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/chatglm2-arc.gif"></a>
-    </td>
-    <td>
-      <a href="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"><img src="https://llm-assets.readthedocs.io/en/latest/_images/llama2-13b-arc.gif"></a>
-    </td>
-  </tr>
-  <tr>
-    <td align="center" width="25%"><code>chatglm2-6b</code></td>
-    <td align="center" width="25%"><code>llama-2-13b-chat</code></td>
-    <td align="center" width="25%"><code>chatglm2-6b</code></td>
-    <td align="center" width="25%"><code>llama-2-13b-chat</code></td>
-  </tr>
-</table>
-
-### Verified models
-Over 20 models have been optimized/verified on `ipex-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Dolly, StarCoder, Whisper, Baichuan, InternLM, QWen, Aquila, MOSS,* and more; see the complete list below.
-  
-| Model      | CPU Example                                                    | GPU Example                                                     |
-|------------|----------------------------------------------------------------|-----------------------------------------------------------------|
-| LLaMA *(such as Vicuna, Guanaco, Koala, Baize, WizardLM, etc.)* | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/vicuna) |[link](example/GPU/HF-Transformers-AutoModels/Model/vicuna)|
-| LLaMA 2    | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/llama2) | [link1](example/GPU/HF-Transformers-AutoModels/Model/llama2), [link2-low GPU memory example](example/GPU/PyTorch-Models/Model/llama2#example-2---low-memory-version-predict-tokens-using-generate-api) |
-| ChatGLM    | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm)   |    | 
-| ChatGLM2   | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm2)  | [link](example/GPU/HF-Transformers-AutoModels/Model/chatglm2)   |
-| ChatGLM3   | [link](example/CPU/HF-Transformers-AutoModels/Model/chatglm3)  | [link](example/GPU/HF-Transformers-AutoModels/Model/chatglm3)   |
-| Mistral    | [link](example/CPU/HF-Transformers-AutoModels/Model/mistral)   | [link](example/GPU/HF-Transformers-AutoModels/Model/mistral)    |
-| Falcon     | [link](example/CPU/HF-Transformers-AutoModels/Model/falcon)    | [link](example/GPU/HF-Transformers-AutoModels/Model/falcon)     |
-| MPT        | [link](example/CPU/HF-Transformers-AutoModels/Model/mpt)       | [link](example/GPU/HF-Transformers-AutoModels/Model/mpt)        |
-| Dolly-v1   | [link](example/CPU/HF-Transformers-AutoModels/Model/dolly_v1)  | [link](example/GPU/HF-Transformers-AutoModels/Model/dolly_v1)   | 
-| Dolly-v2   | [link](example/CPU/HF-Transformers-AutoModels/Model/dolly_v2)  | [link](example/GPU/HF-Transformers-AutoModels/Model/dolly_v2)   | 
-| Replit Code| [link](example/CPU/HF-Transformers-AutoModels/Model/replit)    | [link](example/GPU/HF-Transformers-AutoModels/Model/replit)     |
-| RedPajama  | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/redpajama) |    | 
-| Phoenix    | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/phoenix)   |    | 
-| StarCoder  | [link1](example/CPU/Native-Models), [link2](example/CPU/HF-Transformers-AutoModels/Model/starcoder) | [link](example/GPU/HF-Transformers-AutoModels/Model/starcoder) | 
-| Baichuan   | [link](example/CPU/HF-Transformers-AutoModels/Model/baichuan)  | [link](example/GPU/HF-Transformers-AutoModels/Model/baichuan)   |
-| Baichuan2  | [link](example/CPU/HF-Transformers-AutoModels/Model/baichuan2) | [link](example/GPU/HF-Transformers-AutoModels/Model/baichuan2)  |
-| InternLM   | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm)  | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm)   |
-| Qwen       | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen)      | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen)       | 
-| Qwen1.5 | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen1.5) | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen1.5) |
-| Qwen-VL    | [link](example/CPU/HF-Transformers-AutoModels/Model/qwen-vl)   | [link](example/GPU/HF-Transformers-AutoModels/Model/qwen-vl)    |
-| Aquila     | [link](example/CPU/HF-Transformers-AutoModels/Model/aquila)    | [link](example/GPU/HF-Transformers-AutoModels/Model/aquila)     |
-| Aquila2    | [link](example/CPU/HF-Transformers-AutoModels/Model/aquila2)   | [link](example/GPU/HF-Transformers-AutoModels/Model/aquila2)    |
-| MOSS       | [link](example/CPU/HF-Transformers-AutoModels/Model/moss)      |    | 
-| Whisper    | [link](example/CPU/HF-Transformers-AutoModels/Model/whisper)   | [link](example/GPU/HF-Transformers-AutoModels/Model/whisper)    |
-| Phi-1_5    | [link](example/CPU/HF-Transformers-AutoModels/Model/phi-1_5)   | [link](example/GPU/HF-Transformers-AutoModels/Model/phi-1_5)    |
-| Flan-t5    | [link](example/CPU/HF-Transformers-AutoModels/Model/flan-t5)   | [link](example/GPU/HF-Transformers-AutoModels/Model/flan-t5)    |
-| LLaVA      | [link](example/CPU/PyTorch-Models/Model/llava)                 | [link](example/GPU/PyTorch-Models/Model/llava)                  |
-| CodeLlama  | [link](example/CPU/HF-Transformers-AutoModels/Model/codellama) | [link](example/GPU/HF-Transformers-AutoModels/Model/codellama)  |
-| Skywork    | [link](example/CPU/HF-Transformers-AutoModels/Model/skywork)                 |    |
-| InternLM-XComposer    | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer)   |   |
-| WizardCoder-Python | [link](example/CPU/HF-Transformers-AutoModels/Model/wizardcoder-python) | |
-| CodeShell | [link](example/CPU/HF-Transformers-AutoModels/Model/codeshell) | |
-| Fuyu      | [link](example/CPU/HF-Transformers-AutoModels/Model/fuyu) | |
-| Distil-Whisper | [link](example/CPU/HF-Transformers-AutoModels/Model/distil-whisper) | [link](example/GPU/HF-Transformers-AutoModels/Model/distil-whisper) |
-| Yi | [link](example/CPU/HF-Transformers-AutoModels/Model/yi) | [link](example/GPU/HF-Transformers-AutoModels/Model/yi) |
-| BlueLM | [link](example/CPU/HF-Transformers-AutoModels/Model/bluelm) | [link](example/GPU/HF-Transformers-AutoModels/Model/bluelm) |
-| Mamba | [link](example/CPU/PyTorch-Models/Model/mamba) | [link](example/GPU/PyTorch-Models/Model/mamba) |
-| SOLAR | [link](example/CPU/HF-Transformers-AutoModels/Model/solar) | [link](example/GPU/HF-Transformers-AutoModels/Model/solar) |
-| Phixtral | [link](example/CPU/HF-Transformers-AutoModels/Model/phixtral) | [link](example/GPU/HF-Transformers-AutoModels/Model/phixtral) |
-| InternLM2 | [link](example/CPU/HF-Transformers-AutoModels/Model/internlm2) | [link](example/GPU/HF-Transformers-AutoModels/Model/internlm2) |
-| RWKV4 |  | [link](example/GPU/HF-Transformers-AutoModels/Model/rwkv4) |
-| RWKV5 |  | [link](example/GPU/HF-Transformers-AutoModels/Model/rwkv5) |
-| Bark | [link](example/CPU/PyTorch-Models/Model/bark) | [link](example/GPU/PyTorch-Models/Model/bark) |
-| SpeechT5 |  | [link](example/GPU/PyTorch-Models/Model/speech-t5) |
-| DeepSeek-MoE | [link](example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe) |  |
-| Ziya-Coding-34B-v1.0 | [link](example/CPU/HF-Transformers-AutoModels/Model/ziya) | |
-| Phi-2 | [link](example/CPU/HF-Transformers-AutoModels/Model/phi-2) | [link](example/GPU/HF-Transformers-AutoModels/Model/phi-2) |
-| Yuan2 | [link](example/CPU/HF-Transformers-AutoModels/Model/yuan2) | [link](example/GPU/HF-Transformers-AutoModels/Model/yuan2) |
-| DeciLM-7B | [link](example/CPU/HF-Transformers-AutoModels/Model/deciLM-7b) | [link](example/GPU/HF-Transformers-AutoModels/Model/deciLM-7b) |
-| Deepseek | [link](example/CPU/HF-Transformers-AutoModels/Model/deepseek) | [link](example/GPU/HF-Transformers-AutoModels/Model/deepseek) |
-
-### Working with `ipex-llm`
-
-<details><summary>Table of Contents</summary>
-
-- [IPEX-LLM](#ipex-llm)
-  - [Demos](#demos)
-  - [Verified models](#verified-models)
-  - [Working with `ipex-llm`](#working-with-ipex-llm)
-    - [Install](#install)
-      - [CPU](#cpu)
-      - [GPU](#gpu)
-    - [Run Model](#run-model)
-      - [1. Hugging Face `transformers` API](#1-hugging-face-transformers-api)
-        - [CPU INT4](#cpu-int4)
-        - [GPU INT4](#gpu-int4)
-        - [More Low-Bit Support](#more-low-bit-support)
-      - [2. Native INT4 model](#2-native-int4-model)
-      - [3. LangChain API](#3-langchain-api)
-      - [4. CLI Tool](#4-cli-tool)
-  - [`ipex-llm` API Doc](#ipex-llm-api-doc)
-
-</details>
-
-#### Install
-##### CPU
-You may install **`ipex-llm`** on Intel CPU as follows:
-```bash
-pip install --pre --upgrade ipex-llm[all]
-```
-> Note: `ipex-llm` has been tested on Python 3.9
-
-##### GPU
-You may install **`ipex-llm`** on Intel GPU as follows:
-```bash
-# 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 ipex-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
-```
-> Note: `ipex-llm` has been tested on Python 3.9
-
-#### Run Model
- 
-You may run the models using **`ipex-llm`** through one of the following APIs:
-1. [Hugging Face `transformers` API](#1-hugging-face-transformers-api)
-2. [Native INT4 Model](#2-native-int4-model)
-3. [LangChain API](#3-langchain-api)
-4. [CLI (command line interface) Tool](#4-cli-tool)
-
-##### 1. Hugging Face `transformers` API
-You may run any Hugging Face *Transformers* model as follows:
-
-###### CPU INT4
-You may apply INT4 optimizations to any Hugging Face *Transformers* model on Intel CPU as follows.
-
-```python
-#load Hugging Face Transformers model with INT4 optimizations
-from ipex_llm.transformers import AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_4bit=True)
-
-#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)
-```
-
-See the complete examples [here](example/CPU/HF-Transformers-AutoModels/Model/).  
-
-###### GPU INT4
-You may apply INT4 optimizations to any Hugging Face *Transformers* model on Intel GPU as follows.
-
-```python
-#load Hugging Face Transformers model with INT4 optimizations
-from ipex_llm.transformers import AutoModelForCausalLM
-import intel_extension_for_pytorch
-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](example/GPU).
-
-###### More Low-Bit Support
-- Save and load
-
-  After the model is optimized using `ipex-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](example/CPU/HF-Transformers-AutoModels/Save-Load).*
-
-- 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](example/CPU/HF-Transformers-AutoModels/More-Data-Types).*
-
-##### 2. Native INT4 model
- 
-You may also convert Hugging Face *Transformers* models into native INT4 model format for maximum performance as follows.
-
->**Notes**: Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; for other models, you may use the Hugging Face `transformers` model format as described above).
-  
-```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)
-``` 
-
-See the complete example [here](example/CPU/Native-Models/native_int4_pipeline.py). 
-
-##### 3. LangChain API
-You may run the models using the LangChain API in `ipex-llm`.
-
-- **Using Hugging Face `transformers` model**
-
-  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(...)
-  ```
-  See the examples [here](example/CPU/LangChain/transformers_int4).
- 
-- **Using native INT4 model**
-
-  You may also convert Hugging Face *Transformers* models into *native INT4* format, and then run the converted models using the LangChain API as follows.
-  
-  >**Notes**:* Currently only llama/bloom/gptneox/starcoder/chatglm model families are supported; for other models, you may use the Hugging Face `transformers` model format as described above).
-
-  ```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 ChatGLMEmbeddings/GptneoxEmbeddings/BloomEmbeddings/StarcoderEmbeddings to load other models
-  embeddings = LlamaEmbeddings(model_path='/path/to/converted/model.bin')
-  #switch to ChatGLMLLM/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(...)
-  ```
-
-  See the examples [here](example/CPU/LangChain/native_int4).
-
-##### 4. CLI Tool
->**Note**: Currently `ipex-llm` CLI supports *LLaMA* (e.g., *vicuna*), *GPT-NeoX* (e.g., *redpajama*), *BLOOM* (e.g., *pheonix*) and *GPT2* (e.g., *starcoder*) model architecture; for other models, you may use the Hugging Face `transformers` or LangChain APIs.
-
- - ##### Convert model
- 
-    You may convert the downloaded model into native INT4 format using `llm-convert`.
-    
-   ```bash
-   #convert PyTorch (fp16 or fp32) model; 
-   #llama/bloom/gptneox/starcoder model family is currently supported
-   llm-convert "/path/to/model/" --model-format pth --model-family "bloom" --outfile "/path/to/output/"
-
-   #convert GPTQ-4bit model
-   #only llama model family is currently supported
-   llm-convert "/path/to/model/" --model-format gptq --model-family "llama" --outfile "/path/to/output/"
-   ```  
-  
- - ##### Run model
-   
-   You may run the converted model using `llm-cli` or `llm-chat` (*built on top of `main.cpp` in [llama.cpp](https://github.com/ggerganov/llama.cpp)*)
-
-   ```bash
-   #help
-   #llama/bloom/gptneox/starcoder model family is currently supported
-   llm-cli -x gptneox -h
-
-   #text completion
-   #llama/bloom/gptneox/starcoder model family is currently supported
-   llm-cli -t 16 -x gptneox -m "/path/to/output/model.bin" -p 'Once upon a time,'
-
-   #chat mode
-   #llama/gptneox model family is currently supported
-   llm-chat -m "/path/to/output/model.bin" -x llama
-   ```
-
-### `ipex-llm` API Doc
-See the inital `ipex-llm` API Doc [here](https://ipex-llm.readthedocs.io/en/latest/doc/PythonAPI/LLM/index.html).
-
-[^1]: Performance varies by use, configuration and other factors. `ipex-llm` may not optimize to the same degree for non-Intel products. Learn more at www.Intel.com/PerformanceIndex.