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Update readme (#10378)

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> *It is built on the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq](https://github.com/IST-DASLab/gptq), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [awq](https://github.com/mit-han-lab/llm-awq), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [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.* > *It is built on the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq](https://github.com/IST-DASLab/gptq), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [awq](https://github.com/mit-han-lab/llm-awq), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [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.*
### Latest update 🔥 ### Latest update 🔥
- [2024/03] **LangChain** added support for `bigdl-llm`; see the details [here](https://python.langchain.com/docs/integrations/llms/bigdl).
- [2024/02] `bigdl-llm` now supports directly loading model from [ModelScope](python/llm/example/GPU/ModelScope-Models) ([魔搭](python/llm/example/CPU/ModelScope-Models)). - [2024/02] `bigdl-llm` now supports directly loading model from [ModelScope](python/llm/example/GPU/ModelScope-Models) ([魔搭](python/llm/example/CPU/ModelScope-Models)).
- [2024/02] `bigdl-llm` added inital **INT2** support (based on llama.cpp [IQ2](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2) mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM. - [2024/02] `bigdl-llm` added inital **INT2** support (based on llama.cpp [IQ2](python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2) mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.
- [2024/02] Users can now use `bigdl-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI. - [2024/02] Users can now use `bigdl-llm` through [Text-Generation-WebUI](https://github.com/intel-analytics/text-generation-webui) GUI.

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docs/readthedocs/source/index.rst
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@ -24,6 +24,7 @@ BigDL-LLM
============================================ ============================================
Latest update 🔥 Latest update 🔥
============================================ ============================================
- [2024/03] **LangChain** added support for ``bigdl-llm``; see the details `here <https://python.langchain.com/docs/integrations/llms/bigdl>`_.
- [2024/02] ``bigdl-llm`` now supports directly loading model from `ModelScope <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/ModelScope-Models>`_ (`魔搭 <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/ModelScope-Models>`_). - [2024/02] ``bigdl-llm`` now supports directly loading model from `ModelScope <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/ModelScope-Models>`_ (`魔搭 <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/ModelScope-Models>`_).
- [2024/02] ``bigdl-llm`` added inital **INT2** support (based on llama.cpp `IQ2 <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2>`_ mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM. - [2024/02] ``bigdl-llm`` added inital **INT2** support (based on llama.cpp `IQ2 <https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Advanced-Quantizations/GGUF-IQ2>`_ mechanism), which makes it possible to run large-size LLM (e.g., Mixtral-8x7B) on Intel GPU with 16GB VRAM.
- [2024/02] Users can now use ``bigdl-llm`` through `Text-Generation-WebUI <https://github.com/intel-analytics/text-generation-webui>`_ GUI. - [2024/02] Users can now use ``bigdl-llm`` through `Text-Generation-WebUI <https://github.com/intel-analytics/text-generation-webui>`_ GUI.