model

README.md

@@ -2,8 +2,7 @@
 language:
   - en
   - zh
-license_name: baichuan2-community-license
-license_link: https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/main/Community%20License%20for%20Baichuan2%20Model.pdf
+license: other
 tasks:
   - text-generation
 ---
@@ -20,7 +19,6 @@ tasks:
 <a href="https://github.com/baichuan-inc/Baichuan2" target="_blank">🦉GitHub</a> | <a href="https://github.com/baichuan-inc/Baichuan-7B/blob/main/media/wechat.jpeg?raw=true" target="_blank">💬WeChat</a>
 </div>
 <div align="center">
-  百川API支持搜索增强和192K长窗口，新增百川搜索增强知识库、限时免费！<br>
 🚀 <a href="https://www.baichuan-ai.com/" target="_blank">百川大模型在线对话平台</a> 已正式向公众开放 🎉
 </div>
 
@@ -29,7 +27,6 @@ tasks:
 - [📖 模型介绍/Introduction](#Introduction)
 - [⚙️ 快速开始/Quick Start](#Start)
 - [📊 Benchmark评估/Benchmark Evaluation](#Benchmark)
-- [👥 社区与生态/Community](#Community)
 - [📜 声明与协议/Terms and Conditions](#Terms)
 
 
@@ -118,16 +115,6 @@ In addition to the [Baichuan2-7B-Base](https://huggingface.co/baichuan-inc/Baich
 
 ![checkpoint](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/checkpoints.jpeg)
 
-# <span id="Community">社区与生态/Community</span>
-
-## Intel 酷睿 Ultra 平台运行百川大模型
-
-使用酷睿™/至强® 可扩展处理器或配合锐炫™ GPU等进行部署[Baichuan2-7B-Chat]，[Baichuan2-13B-Chat]模型，推荐使用 BigDL-LLM([CPU], [GPU])以发挥更好推理性能。
-
-详细支持信息可参考[中文操作手册](https://github.com/intel-analytics/bigdl-llm-tutorial/tree/main/Chinese_Version)，包括用notebook支持，[加载，优化，保存方法](https://github.com/intel-analytics/bigdl-llm-tutorial/blob/main/Chinese_Version/ch_3_AppDev_Basic/3_BasicApp.ipynb)等。
-
-When deploy on Core™/Xeon® Scalable Processors or with Arc™ GPU, BigDL-LLM ([CPU], [GPU]) is recommended to take full advantage of better inference performance.
-
 # <span id="Terms">声明与协议/Terms and Conditions</span>
 
 ## 声明
@@ -145,21 +132,9 @@ We have done our best to ensure the compliance of the data used in the model tra
 
 ## 协议
 
-社区使用 Baichuan 2 模型需要遵循 [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) 和[《Baichuan 2 模型社区许可协议》](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf)。Baichuan 2 模型支持商业用途，如果您计划将 Baichuan 2 模型或其衍生品用于商业目的，请您确认您的主体符合以下情况：
-  1. 您或您的关联方的服务或产品的日均用户活跃量（DAU）低于100万。
-  2. 您或您的关联方不是软件服务提供商、云服务提供商。
-  3. 您或您的关联方不存在将授予您的商用许可，未经百川许可二次授权给其他第三方的可能。
-
-在符合以上条件的前提下，您需要通过以下联系邮箱 [email protected] ，提交《Baichuan 2 模型社区许可协议》要求的申请材料。审核通过后，百川将特此授予您一个非排他性、全球性、不可转让、不可再许可、可撤销的商用版权许可。
-
-The community usage of Baichuan 2 model requires adherence to [Apache 2.0](https://github.com/baichuan-inc/Baichuan2/blob/main/LICENSE) and [Community License for Baichuan2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/resolve/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). The Baichuan 2 model supports commercial use. If you plan to use the Baichuan 2 model or its derivatives for commercial purposes, please ensure that your entity meets the following conditions:
-
-  1. The Daily Active Users (DAU) of your or your affiliate's service or product is less than 1 million.
-  2. Neither you nor your affiliates are software service providers or cloud service providers.
-  3. There is no possibility for you or your affiliates to grant the commercial license given to you, to reauthorize it to other third parties without Baichuan's permission.
-
-Upon meeting the above conditions, you need to submit the application materials required by the Baichuan 2 Model Community License Agreement via the following contact email: [email protected]. Once approved, Baichuan will hereby grant you a non-exclusive, global, non-transferable, non-sublicensable, revocable commercial copyright license.
+Baichuan 2 模型的社区使用需遵循[《Baichuan 2 模型社区许可协议》]。Baichuan 2 支持商用。如果将 Baichuan 2 模型或其衍生品用作商业用途，请您按照如下方式联系许可方，以进行登记并向许可方申请书面授权：联系邮箱 [[email protected]]。
 
+The use of the source code in this repository follows the open-source license Apache 2.0. Community use of the Baichuan 2 model must adhere to the [Community License for Baichuan 2 Model](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Baichuan%202%E6%A8%A1%E5%9E%8B%E7%A4%BE%E5%8C%BA%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf). Baichuan 2 supports commercial use. If you are using the Baichuan 2 models or their derivatives for commercial purposes, please contact the licensor in the following manner for registration and to apply for written authorization: Email [email protected].
 
 [GitHub]:https://github.com/baichuan-inc/Baichuan2
 [Baichuan2]:https://github.com/baichuan-inc/Baichuan2
@@ -187,6 +162,3 @@ Upon meeting the above conditions, you need to submit the application materials
 [[email protected]]: mailto:[email protected]
 [训练过程heckpoint下载]: https://huggingface.co/baichuan-inc/Baichuan2-7B-Intermediate-Checkpoints
 [百川智能]: https://www.baichuan-ai.com
-
-[CPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/CPU/HF-Transformers-AutoModels/Model/baichuan2
-[GPU]: https://github.com/intel-analytics/BigDL/tree/main/python/llm/example/GPU/HF-Transformers-AutoModels/Model/baichuan2

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<pad>": 125696
+}

modeling_baichuan.py

@@ -502,7 +502,6 @@ class NormHead(nn.Module):
     def forward(self, hidden_states):
         if self.training:
             norm_weight = nn.functional.normalize(self.weight)
-            self.first_flag = True
         elif self.first_flag:
             self.first_flag = False
             self.weight.data = nn.functional.normalize(self.weight)

tokenization_baichuan.py

@@ -72,13 +72,6 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
         unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
         pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-        
-        self.vocab_file = vocab_file
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
-        self.sp_model.Load(vocab_file)        
-        
         super().__init__(
             bos_token=bos_token,
             eos_token=eos_token,
@@ -90,6 +83,11 @@ class BaichuanTokenizer(PreTrainedTokenizer):
             clean_up_tokenization_spaces=clean_up_tokenization_spaces,
             **kwargs,
         )
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
 
     def __getstate__(self):
         state = self.__dict__.copy()