Miniconda/Anaconda -> Miniforge update in examples (#11194)

* Change installation address

Change former address: "https://docs.conda.io/en/latest/miniconda.html#" to new address: "https://conda-forge.org/download/" for 63 occurrences under python\llm\example

* Change Prompt

Change "Anaconda Prompt" to "Miniforge Prompt" for 1 occurrence

python/llm/example/CPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -21,7 +21,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 In the example [generate.py](./generate.py), we show a basic use case to load a GGUF LLaMA2 model into `ipex-llm` using `from_gguf()` API, with IPEX-LLM optimizations.
 
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Aquila model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/aquila2/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Aquila2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/chatglm/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/codegemma/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a CodeGemma model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/codeshell/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a CodeShell model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/deepseek-moe/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a DeepSeek-MoE model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/distil-whisper/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Recognize Tokens using `generate()` API
 In the example [recognize.py](./recognize.py), we show a basic use case for a Distil-Whisper model to conduct transcription using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/flan-t5/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Flan-t5 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/fuyu/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Fuyu model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/internlm-xcomposer/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Multi-turn chat centered around an image using `chat()` API
 In the example [chat.py](./chat.py), we show a basic use case for an InternLM_XComposer model to start a multi-turn chat centered around an image using `chat()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/mistral/README.md

@@ -9,7 +9,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Mistral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/mixtral/README.md

@@ -9,7 +9,7 @@ To run these examples with IPEX-LLM on Intel CPUs, we have some recommended requ
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Mixtral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-1_5/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-1_5 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-2/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/phi-3/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/phixtral/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phixtral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/qwen-vl/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Multimodal chat using `chat()` API
 In the example [chat.py](./chat.py), we show a basic use case for a Qwen-VL model to start a multimodal chat using `chat()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/replit/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Replit model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/stablelm/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a StableLM model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/yi/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Yi model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/yuan2/README.md

@@ -9,7 +9,7 @@ In addition, you need to modify some files in Yuan2-2B-hf folder, since Flash at
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Yuan2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/HF-Transformers-AutoModels/Model/ziya/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Ziya model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/aquila2/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Aquila2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/bark/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Synthesize speech with the given input text
 In the example [synthesize_speech.py](./synthesize_speech.py), we show a basic use case for Bark model to synthesize speech based on the given text, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/bert/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Extract the feature of given text
 In the example [extract_feature.py](./extract_feature.py), we show a basic use case for a BERT model to extract the feature of given text, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/bluelm/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a BlueLM model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/chatglm/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/chatglm3/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a ChatGLM3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/codegemma/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a CodeGemma model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/codellama/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a CodeLlama model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/codeshell/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a CodeShell model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/deciLM-7b/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a DeciLM-7B model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/deepseek-moe/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a deepseek-moe model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/deepseek/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Deepseek model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/distil-whisper/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Recognize Tokens using `generate()` API
 In the example [recognize.py](./recognize.py), we show a basic use case for a Distil-Whisper model to conduct transcription using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/flan-t5/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Flan-t5 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/fuyu/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Fuyu model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/internlm-xcomposer/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Multi-turn chat centered around an image using `chat()` API
 In the example [chat.py](./chat.py), we show a basic use case for an InternLM_XComposer model to start a multi-turn chat centered around an image using `chat()` API, with IPEX-LLM 'optimize_model' API.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/internlm2/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a InternLM2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/llama2/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Llama2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/llama3/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Llama3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/llava/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Multi-turn chat centered around an image using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a LLaVA model to start a multi-turn chat centered around an image using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/mamba/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Mamba model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/mistral/README.md

@@ -9,7 +9,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Mistral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/mixtral/README.md

@@ -9,7 +9,7 @@ To run these examples with IPEX-LLM on Intel CPUs, we have some recommended requ
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Mixtral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/phi-1_5/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-1_5 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/phi-2/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/phi-3/README.md

@@ -12,7 +12,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phi-3 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/phixtral/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a phixtral model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/qwen-vl/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Multimodal chat using `chat()` API
 In the example [chat.py](./chat.py), we show a basic use case for a Qwen-VL model to start a multimodal chat using `chat()` API, with IPEX-LLM 'optimize_model' API.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/qwen1.5/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Qwen1.5 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/skywork/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Skywork model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/solar/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a SOLAR model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/stablelm/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a StableLM model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/wizardcoder-python/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a WizardCoder-Python model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/yi/README.md

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Yi model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/yuan2/README.md

@@ -9,7 +9,7 @@ In addition, you need to modify some files in Yuan2-2B-hf folder, since Flash at
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for an Yuan2 model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

python/llm/example/CPU/PyTorch-Models/Model/ziya/README.md

@@ -7,7 +7,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 ## Example: Predict Tokens using `generate()` API
 In the example [generate.py](./generate.py), we show a basic use case for a Ziya model to predict the next N tokens using `generate()` API, with IPEX-LLM 'optimize_model' API.
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 

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

@@ -8,7 +8,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 In this example, we run inference for a Llama2 model to showcase the speed of EAGLE with IPEX-LLM on MT-bench data 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 ```bash

python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF/README.md

@@ -19,7 +19,7 @@ To run these examples with IPEX-LLM, we have some recommended requirements for y
 In the example [generate.py](./generate.py), we show a basic use case to load a GGUF LLaMA2 model into `ipex-llm` using `from_gguf()` API, with IPEX-LLM optimizations.
 
 ### 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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 ```bash

python/llm/example/GPU/HF-Transformers-AutoModels/Model/codegemma/README.md

@@ -10,7 +10,7 @@ To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requ
 In the example [generate.py](./generate.py), we show a basic use case for a CodeGemma model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 ### 1. Install
 #### 1.1 Installation on Linux
-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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 ```bash

python/llm/example/GPU/PyTorch-Models/Model/codegemma/README.md

@@ -10,7 +10,7 @@ To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requ
 In the example [generate.py](./generate.py), we show a basic use case for a CodeGemma model to predict the next N tokens using `generate()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
 ### 1. Install
 #### 1.1 Installation on Linux
-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#).
+We suggest using conda to manage the Python environment. For more information about conda installation, please refer to [here](https://conda-forge.org/download/).
 
 After installing conda, create a Python environment for IPEX-LLM:
 ```bash

python/llm/scripts/README.md

@@ -17,7 +17,7 @@ sudo apt install xpu-smi
   
 ### Usage
 
-* After installing `ipex-llm`, open a terminal (on Linux) or **Anaconda Prompt** (on Windows), and activate the conda environment you have created for running `ipex-llm`: 
+* After installing `ipex-llm`, open a terminal (on Linux) or **Miniforge Prompt** (on Windows), and activate the conda environment you have created for running `ipex-llm`: 
   ```
   conda activate llm
   ```