Merge branch 'main' into main

Signed-off-by: VincyZhang <wenxin.zhang@intel.com>

README.md

@@ -11,11 +11,11 @@ Intel® Extension for Transformers
 </div>
 
 ## 🚀Latest News
-* [2023/10] LLM runtime, an Intel-optimized [GGML](https://github.com/ggerganov/ggml) compatiable runtime, demonstrates **up to 15x performance gain in 1st token generation and 1.5x in other token generation** over the default [llama.cpp](https://github.com/ggerganov/llama.cpp).
-* [2023/10] LLM runtime now supports LLM inference with **infinite-length inputs up to 4 million tokens**, inspired by [StreamingLLM](https://arxiv.org/abs/2309.17453).
+* [2023/10] LLM runtime, an Intel-optimized [GGML](https://github.com/ggerganov/ggml) compatible runtime, demonstrates **up to 15x performance gain in 1st token generation and 1.5x in other token generation** over the default [llama.cpp](https://github.com/ggerganov/llama.cpp).
+* [2023/10] LLM runtime now supports LLM inference with **infinite-length inputs up to 4 million tokens**, inspired from [StreamingLLM](https://arxiv.org/abs/2309.17453).
 * [2023/09] NeuralChat has been showcased in [**Intel Innovation’23 Keynote**](https://www.youtube.com/watch?v=RbKRELWP9y8&t=2954s) and [Google Cloud Next'23](https://cloud.google.com/blog/topics/google-cloud-next/welcome-to-google-cloud-next-23) to demonstrate GenAI/LLM capabilities on Intel Xeon Scalable Processors.
 * [2023/08] NeuralChat supports **custom chatbot development and deployment within minutes** on broad Intel HWs such as Xeon Scalable Processors, Gaudi2, Xeon CPU Max Series, Data Center GPU Max Series, Arc Series, and Core Processors. Check out [Notebooks](./intel_extension_for_transformers/neural_chat/docs/full_notebooks.md).
-* [2023/07] LLM runtime extends the Hugging Face Transformers API to provide seamless low precision inference for popular LLMs, supporting mainstream low precision data types such as INT3/INT4/FP4/NF4/INT5/INT8/FP8.
+* [2023/07] LLM runtime extends Hugging Face Transformers API to provide seamless low precision inference for popular LLMs, supporting low precision data types such as INT3/INT4/FP4/NF4/INT5/INT8/FP8.
 
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 <div align="left">
@@ -28,21 +28,21 @@ pip install intel-extension-for-transformers
 > For more installation methods, please refer to [Installation Page](./docs/installation.md)
 
 ## 🌟Introduction
-Intel® Extension for Transformers is an innovative toolkit to accelerate Transformer-based models on Intel platforms, in particular effective on 4th Intel Xeon Scalable processor Sapphire Rapids (codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html)). The toolkit provides the following key features and examples:
+Intel® Extension for Transformers is an innovative toolkit to accelerate Transformer-based models on Intel platforms, in particular, effective on 4th Intel Xeon Scalable processor Sapphire Rapids (codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html)). The toolkit provides the below key features and examples:
 
-*  Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformer](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
+*  Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor)
 
 *  Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754))
 
-*  Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list) and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) 
+*  Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) 
 
 *  [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of plugins [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md).
 
 *  [Inference](intel_extension_for_transformers/llm/runtime/graph) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels, supporting [GPT-NEOX](intel_extension_for_transformers/llm/runtime/graph/models/gptneox), [LLAMA](intel_extension_for_transformers/llm/runtime/graph/models/llama), [MPT](intel_extension_for_transformers/llm/runtime/graph/models/mpt), [FALCON](intel_extension_for_transformers/llm/runtime/graph/models/falcon), [BLOOM-7B](intel_extension_for_transformers/llm/runtime/graph/models/bloom), [OPT](intel_extension_for_transformers/llm/runtime/graph/models/opt), [ChatGLM2-6B](intel_extension_for_transformers/llm/runtime/graph/models/chatglm), [GPT-J-6B](intel_extension_for_transformers/llm/runtime/graph/models/gptj) and [Dolly-v2-3B](intel_extension_for_transformers/llm/runtime/graph/models/gptneox)
 
 
 ## 🌱Getting Started
-Below is the sample code to enable chatbot. See more [examples](intel_extension_for_transformers/neural_chat/docs/full_notebooks.md).
+Below is the sample code to enable the chatbot. See more [examples](intel_extension_for_transformers/neural_chat/docs/full_notebooks.md).
 
 ### Chatbot 
 ```python
@@ -211,4 +211,5 @@ Find other models like ChatGLM, ChatGLM2, StarCoder... in [LLM Runtime](./intel_
 
 ## 💁Collaborations
 
-Welcome to raise any interesting ideas on model compression techniques and LLM-based chatbot development! Feel free to reach out to [us](mailto:itrex.maintainers@intel.com) and look forward to our collaborations on Intel Extension for Transformers!
+Welcome to raise any interesting ideas on model compression techniques and LLM-based chatbot development! Feel free to reach [us](mailto:itrex.maintainers@intel.com), and we look forward to our collaborations on Intel Extension for Transformers!
+