[RELEASE] Update README.md (PaddlePaddle#7834)

Author: ZHUI

README.md

@@ -30,16 +30,18 @@
 
 ## News 📢
 
-* **2023.8.15 [PaddleNLP v2.6](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.6.0)**： 发布[全流程大模型工具链](./llm)，涵盖预训练，精调，压缩，推理以及部署等各个环节，为用户提供端到端的大模型方案和一站式的开发体验；内置[4D并行分布式Trainer](./docs/trainer.md)，[高效微调算法LoRA/Prefix Tuning](./llm#33-lora), [自研INT8/INT4量化算法](./llm#6-量化)等等；全面支持[LLaMA 1/2](./llm/llama), [BLOOM](.llm/bloom), [ChatGLM 1/2](./llm/chatglm), [GLM](./llm/glm), [OPT](./llm/opt)等主流大模型
+* **2024.01.04 [PaddleNLP v2.7](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.7.1)**： 大模型体验全面升级，统一工具链大模型入口。统一预训练、精调、压缩、推理以及部署等环节的实现代码，到 `PaddleNLP/llm`目录。全新大[模型工具链文档](https://paddlenlp.readthedocs.io/zh/latest/llm/finetune.html)，一站式指引用户从大模型入门到业务部署上线。全断点存储机制 Unified Checkpoint，大大提高大模型存储的通用性。高效微调升级，支持了高效微调+LoRA同时使用，支持了QLoRA等算法。
+
+* **2023.08.15 [PaddleNLP v2.6](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.6.0)**： 发布[全流程大模型工具链](./llm)，涵盖预训练，精调，压缩，推理以及部署等各个环节，为用户提供端到端的大模型方案和一站式的开发体验；内置[4D并行分布式Trainer](./docs/trainer.md)，[高效微调算法LoRA/Prefix Tuning](./llm#33-lora), [自研INT8/INT4量化算法](./llm#6-量化)等等；全面支持[LLaMA 1/2](./llm/llama), [BLOOM](.llm/bloom), [ChatGLM 1/2](./llm/chatglm), [GLM](./llm/glm), [OPT](./llm/opt)等主流大模型
 
 
 ## 安装
 
 ### 环境依赖
 
 - python >= 3.7
-- paddlepaddle >= 2.5.1
-- 如需大模型功能，请使用 paddlepaddle-gpu >= 2.5.1
+- paddlepaddle >= 2.6.0
+- 如需大模型功能，请使用 paddlepaddle-gpu >= 2.6.0
 
 ### pip安装
 

README_en.md

@@ -23,26 +23,31 @@
 
 ## News 📢
 
-* **2023.6.12: [Release of PaddleNLP v2.6rc](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.6.0rc)**
-  * 🔨 LLM Tools：Introduces comprehensive examples of open-source LLM training and inference, including [Bloom](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/bloom), [ChatGLM](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/chatglm), [GLM](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/glm), [Llama](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/llama) and [OPT](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/opt). Added Tensor Parallel capability to [Trainer API](./docs/trainer.md) for distributed LLM trainin. Also released [Parameter-Efficient Finetuning](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/paddlenlp/peft),which enables training LLMs on consumer hardware.
+* **2024.01.04 [PaddleNLP v2.7](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.7.0)**: The LLM experience is fully upgraded, and the tool chain LLM entrance is unified. Unify the implementation code of pre-training, fine-tuning, compression, inference and deployment to the `PaddleNLP/llm` directory. The new [LLM Toolchain Documentation](https://paddlenlp.readthedocs.io/zh/latest/llm/finetune.html) provides one-stop guidance for users from getting started with LLM to business deployment and launch. The full breakpoint storage mechanism Unified Checkpoint greatly improves the versatility of LLM storage. Efficient fine-tuning upgrade supports the simultaneous use of efficient fine-tuning + LoRA, and supports QLoRA and other algorithms.
 
-* **2023.1.12: [Release of PaddleNLP v2.5](<https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.5.0>)**
+* **2023.08.15 [PaddleNLP v2.6](https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.6.0)**: Release [Full-process LLM toolchain](./llm) , covering all aspects of pre-training, fine-tuning, compression, inference and deployment, providing users with end-to-end LLM solutions and one-stop development experience; built-in [4D parallel distributed Trainer](./docs/trainer.md ), [Efficient fine-tuning algorithm LoRA/Prefix Tuning](./llm#33-lora), [Self-developed INT8/INT4 quantization algorithm](./llm#6-quantization), etc.; fully supports [LLaMA 1/2](./llm/llama), [BLOOM](.llm/bloom), [ChatGLM 1/2](./llm/chatglm), [GLM](./llm/glm), [OPT](./llm/opt) and other mainstream LLMs.
 
-    * 🔨 NLP Tools: [PPDiffusers](./ppdiffusers), our cross-modal diffusion model toolbox based on PaddlePaddle, has been released! It provides a complete training process for diffusion models, and supports FastDeploy inference acceleration and multi-hardware deployment (supports Ascend chips and Kunlun core deployment).
-    * 💎 Industrial Applications: Information extraction, text classification, sentiment analysis, and intelligent question answering have all been newly upgraded. New releases include document information extraction [UIE-X](./applications/information_extraction/document), unified text classification [UTC](./applications/zero_shot_text_classification), unified sentiment analysis [UIE-Senta](./applications/sentiment_analysis/unified_sentiment_extraction) , and [unsupervised QA application](./applications/question_answering/unsupervised_qa). At the same time, the [ERNIE 3.0 Tiny v2](./model_zoo/ernie-tiny) series of pretrained small models have been released, which are more effective with low-resource and foreign data. They provide open-source end-to-end deployment solutions such as model pruning, model quantization, FastDeploy inference acceleration, and edge-side deployment to reduce the difficulty of pretrained model deployment.
-    * 💪 Framework Upgrade: Pretrained model [parameter configuration unification](./paddlenlp/transformers/configuration_utils.py), saving and loading custom parameter configurations no longer requires additional development; [Trainer API](./docs/trainer.md) has added BF16 training, recompute recalculations, sharding, and other distributed capabilities. Large-scale pre-training model training can easily be accomplished through simple configuration. [Model Compression API](./docs/compression.md) supports quantization training, vocabulary compression, and other functions. The compressed model has smaller accuracy loss, and the memory consumption of model deployment is greatly reduced. [Data Augmentation API](./docs/dataaug.md) has been comprehensively upgraded to support three granularities of data augmentation strategy: character, word, and sentence, making it easy to customize data augmentation strategies.
-    * 🤝 Community: 🤗Huggingface hub officially supports PaddleNLP pretrained models, supporting PaddleNLP Model and Tokenizer downloads and uploads directly from the 🤗Huggingface hub. Everyone is welcome to try out PaddleNLP pretrained models on the 🤗Huggingface hub [here](https://huggingface.co/PaddlePaddle).
 
-* **September 6, 2022: [Release of PaddleNLP v2.4](<https://github.com/PaddlePaddle/PaddleNLP/releases/tag/v2.4.0>)**
+## Installation
 
-    * 🔨 NLP Tools: [NLP Pipeline System Pipelines](./pipelines) has been released, supporting the rapid construction of search engines and question-answering systems, and can be extended to support various NLP systems, making it easy, flexible, and efficient to solve NLP tasks like building blocks!
-    * 💎 Industrial Applications: A new [text classification full-process application solution](./applications/text_classification) has been added, covering various scenarios such as multi-classification, multi-label, and hierarchical classification, supporting small-sample learning and TrustAI trustworthy computing model training and tuning.
-    * 🍭 AIGC: The SOTA model [CodeGen](https://github.com/PaddlePaddle/PaddleNLP/blob/develop/examples/code_generation/codegen) for code generation in various programming languages has been added.
-    * 💪 Framework Upgrade: [Automatic Model Compression API](./docs/compression.md) has been released, which automatically cuts and quantizes models, greatly reducing the threshold for using model compression technology. [Few-shot Prompt](./applications/text_classification/multi_class/few-shot) capability has been released, integrating classic algorithms such as PET, P-Tuning, and RGL.
+### Prerequisites
 
+* python >= 3.7
+* paddlepaddle >= 2.6.0
 
+More information about PaddlePaddle installation please refer to [PaddlePaddle's Website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/conda/linux-conda.html).
 
+### Python pip Installation
 
+```
+pip install --upgrade paddlenlp
+```
+
+or you can install the latest develop branch code with the following command:
+
+```shell
+pip install --pre --upgrade paddlenlp -f https://www.paddlepaddle.org.cn/whl/paddlenlp.html
+```
 
 
 ## Features
@@ -257,27 +262,6 @@ Set `use_fast=True` to achieve 5x speedup for Transformer, GPT, BART, PLATO, Uni
 For more super large-scale model pre-training details please refer to [GPT-3](./examples/language_model/gpt-3).
 
 
-## Installation
-
-### Prerequisites
-
-* python >= 3.7
-* paddlepaddle >= 2.3
-
-More information about PaddlePaddle installation please refer to [PaddlePaddle's Website](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/conda/linux-conda.html).
-
-### Python pip Installation
-
-```
-pip install --upgrade paddlenlp
-```
-
-or you can install the latest develop branch code with the following command:
-
-```shell
-pip install --pre --upgrade paddlenlp -f https://www.paddlepaddle.org.cn/whl/paddlenlp.html
-```
-
 ## Quick Start
 
 **Taskflow** aims to provide off-the-shelf NLP pre-built task covering NLU and NLG scenario, in the meanwhile with extremely fast inference satisfying industrial applications.

paddlenlp/__init__.py

@@ -18,7 +18,7 @@
 PADDLENLP_STABLE_VERSION = "PADDLENLP_STABLE_VERSION"
 
 
-__version__ = "2.7.0.post"
+__version__ = "2.7.1.post"
 if os.getenv(PADDLENLP_STABLE_VERSION):
     __version__ = __version__.replace(".post", "")
 

setup.py

@@ -109,7 +109,7 @@ def show():
         f.write(content)
 
 
-__version__ = "2.7.0.post"
+__version__ = "2.7.1.post"
 if os.getenv(PADDLENLP_STABLE_VERSION):
     __version__ = __version__.replace(".post", "")