Eagle Speculative Sampling examples

python/llm/example/CPU/Speculative-Decoding/Eagle/README.md

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+# Eagle - Speculative Sampling using IPEX-LLM on Intel CPUs
+IPEX-LLM supports EAGLE (Extrapolation Algorithm for Greater Language-model Efficiency) which is a speculative sampling method that improves text generation speed.
+
+See [here](https://arxiv.org/abs/2401.15077) to view the paper and [here](https://github.com/SafeAILab/EAGLE) for more info on EAGLE code.
+
+## Requirements
+To run these examples with IPEX-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
+
+## Example - EAGLE Speculative Sampling with IPEX-LLM on MT-bench
+In this example, we run inference for a Llama2 model to showcase the speed of EAGLE with IPEX-LLM on MT-bench on Intel CPUs.
+
+### 1. Install
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://docs.conda.io/en/latest/miniconda.html#).
+
+After installing conda, create a Python environment for IPEX-LLM:
+```bash
+conda create -n llm python=3.11 # recommend to use Python 3.11
+conda activate llm
+
+pip install --pre --upgrade ipex-llm[all] 
+pip install intel_extension_for_pytorch==2.1.0 
+pip install -r requirements.txt
+pip install eagle-llm
+```
+
+### 2. Configures IPEX-LLM environment variables for Linux
+
+> [!NOTE]
+> Skip this step if you are running on Windows.
+```bash
+# set IPEX-LLM env variables
+source ipex-llm-init
+
+```
+### 3. Running Example
+You can test the speed of EAGLE speculative sampling with ipex-llm on MT-bench using the following command.
+```bash
+python -m evaluation.gen_ea_answer_llama2chat\
+                 --ea-model-path [path of EAGLE weight]\
+                 --base-model-path [path of the original model]\
+                 --enable-ipex-llm\
+```
+Please refer to [here](https://github.com/SafeAILab/EAGLE#eagle-weights) for the complete list of available EAGLE weights.
+
+The above command will generate a .jsonl file that records the generation results and wall time. Then, you can use evaluation/speed.py to calculate the speed.
+```bash
+python -m evaluation.speed\
+                 --base-model-path [path of the original model]\
+                 --jsonl-file [pathname of the .jsonl file]\
+```
+