diff --git a/README.md b/README.md index 468e0555..dd6d3350 100644 --- a/README.md +++ b/README.md @@ -202,7 +202,7 @@ CPU docker image registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch2.0.1-tf2.13.0-1.9.5 ``` GPU docker image @@ -211,7 +211,7 @@ GPU docker image registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.8.0-py38-torch2.0.1-tf2.13.0-1.9.5 ``` ## Setup Local Python Environment @@ -220,7 +220,7 @@ One can also set up local ModelScope environment using pip and conda. ModelScop We suggest [anaconda](https://docs.anaconda.com/anaconda/install/) for creating local python environment: ```shell -conda create -n modelscope python=3.9 +conda create -n modelscope python=3.8 conda activate modelscope ``` diff --git a/README_ja.md b/README_ja.md index 073b0c48..4523add4 100644 --- a/README_ja.md +++ b/README_ja.md @@ -208,7 +208,7 @@ CPU docker イメージ registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch2.0.1-tf2.13.0-1.9.5 ``` GPU docker イメージ @@ -217,7 +217,7 @@ GPU docker イメージ registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.8.0-py38-torch2.0.1-tf2.13.0-1.9.5 ``` ## ローカル Python 環境のセットアップ diff --git a/README_zh.md b/README_zh.md index 77fe684a..10b2e728 100644 --- a/README_zh.md +++ b/README_zh.md @@ -53,19 +53,19 @@ ModelScope开源了数百个(当前700+)模型，涵盖自然语言处理、计 自然语言处理： * [ChatGLM3-6B](https://modelscope.cn/models/ZhipuAI/chatglm3-6b/summary) - + * [Qwen-14B-Chat](https://modelscope.cn/models/qwen/Qwen-14B-Chat/summary) - + * [Baichuan2-13B-Chat](https://modelscope.cn/models/baichuan-inc/Baichuan2-13B-Chat/summary) - + * [Ziya2-13B-Chat](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Chat/summary) - + * [Internlm-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-20b/summary) - + * [Udever-bloom-1b1](https://modelscope.cn/models/damo/udever-bloom-1b1/summary) - + * [CoROM文本向量-中文-电商领域-base](https://modelscope.cn/models/damo/nlp_corom_sentence-embedding_chinese-base-ecom/summary) - + * [MGeo地址相似度匹配实体对齐-中文-地址领域-base](https://modelscope.cn/models/damo/mgeo_geographic_entity_alignment_chinese_base/summary) 多模态： @@ -84,7 +84,7 @@ ModelScope开源了数百个(当前700+)模型，涵盖自然语言处理、计 计算机视觉： * [DamoFD人脸检测关键点模型-0.5G](https://modelscope.cn/models/damo/cv_ddsar_face-detection_iclr23-damofd/summary) - + * [BSHM人像抠图](https://modelscope.cn/models/damo/cv_unet_image-matting/summary) * [DCT-Net人像卡通化-3D](https://modelscope.cn/models/damo/cv_unet_person-image-cartoon-3d_compound-models/summary) @@ -195,7 +195,7 @@ CPU镜像 registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-py38-torch2.0.1-tf2.13.0-1.9.5 ``` GPU镜像 @@ -204,14 +204,14 @@ GPU镜像 registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py37-torch1.11.0-tf1.15.5-1.6.1 # py38 -registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.3.0-py38-torch1.11.0-tf1.15.5-1.6.1 +registry.cn-hangzhou.aliyuncs.com/modelscope-repo/modelscope:ubuntu20.04-cuda11.8.0-py38-torch2.0.1-tf2.13.0-1.9.5 ``` ## 搭建本地Python环境 -你也可以使用pip和conda搭建本地python环境，我们推荐使用[Anaconda](https://docs.anaconda.com/anaconda/install/)，安装完成后，执行如下命令为modelscope library创建对应的python环境： +你也可以使用pip和conda搭建本地python环境，ModelScope支持python3.7+以上环境，我们推荐使用[Anaconda](https://docs.anaconda.com/anaconda/install/)，安装完成后，执行如下命令为modelscope library创建对应的python环境： ```shell -conda create -n modelscope python=3.7 +conda create -n modelscope python=3.8 conda activate modelscope ``` diff --git a/examples/apps/llm_riddles/app.py b/examples/apps/llm_riddles/app.py index 164faeee..e013ad7a 100644 --- a/examples/apps/llm_riddles/app.py +++ b/examples/apps/llm_riddles/app.py @@ -115,8 +115,8 @@ def generate_response(input, model_name): def on_submit(input, model_name, state): # model_name = os.environ.get('MODEL', 'qwen-plus') name_map = { - '通义千问max': 'qwen-max', - '通义千问plus': 'qwen-plus', + 'qwen-max': 'qwen-max', + 'qwen-plus': 'qwen-plus', 'chatglm-turbo': 'chatglm_turbo', } gen_fn = functools.partial( @@ -176,8 +176,8 @@ with block as demo: model_selector = gr.Dropdown( label='选择模型', - choices=['通义千问max', '通义千问plus', 'chatglm-turbo'], - value='通义千问plus') + choices=['qwen-max', 'qwen-plus', 'chatglm-turbo'], + value='qwen-max') question_info = gr.Markdown( update_question_info(current_chapter_index, current_challenge_index)) challenge_info = gr.Textbox( diff --git a/examples/apps/llm_riddles/challenges/ch3.py b/examples/apps/llm_riddles/challenges/ch3.py index d368eba2..bb8040df 100644 --- a/examples/apps/llm_riddles/challenges/ch3.py +++ b/examples/apps/llm_riddles/challenges/ch3.py @@ -1,4 +1,5 @@ import re +import unicodedata def validate_palindrome_inverse(origin_input, response, generate_response): @@ -86,5 +87,15 @@ challenge3 = { (all(char not in input for char in ['狗', '汪', 'dog']) and response.strip() == '汪~') }, + { + 'title': + '第7题 魔搭 （🐶）', + 'description': + '请提一个不包含任何中文字符、换行或空格的问题，使模型正好回答"魔搭"。', + 'validator': + lambda response, input: not any( + 'CJK' in unicodedata.name(char, '') or char in '\t\n ' + for char in input) and (response.strip() == '魔搭') + }, ] }