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--- |
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license: gemma |
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widget: |
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- messages: |
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- role: user |
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content: 大脑是如何工作的? |
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inference: |
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parameters: |
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max_new_tokens: 2048 |
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language: |
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- en |
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- zh |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- gemma |
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- chinese |
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- sft |
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--- |
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# Updates |
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- [June 04, 2024] 🌈 Add more Chinese training data to improve Chinese performance |
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- [May 23, 2024] 🔥 Support function calling |
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# Model Summary |
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[Gemma-1.1-7B-Chinese-Chat](https://huggingface.co/ycjcl868/Gemma-1.1-7B-Chinese-Chat) is an instruction-tuned language model for Chinese & English users built upon the gemma-1.1-7b-it model.([Github](https://github.com/ycjcl868/Gemma-1.1-7B-Chinese-Chat/tree/main)) |
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Developed by: [ycjcl868](https://github.com/ycjcl868) |
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- License: [Gemma License](https://www.kaggle.com/models/google/gemma/license/consent) |
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- Base Model: gemma-1.1-7b-it |
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- Model Size: 8.54B |
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- Context length: 8K |
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# Introduction |
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This is the first model specifically fine-tuned for Chinese & English user through SFT based on the [gemma-1.1-7b-it model](https://huggingface.co/google/gemma-1.1-7b-it). |
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**Compared to the original [gemma-1.1-7b-it model](https://huggingface.co/google/gemma-1.1-7b-it), our Gemma-1.1-7B-Chinese-Chat model significantly reduces the issues of "Chinese questions with English answers" and the mixing of Chinese and English in responses.** |
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Training details: |
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- epochs: 3 |
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- learning rate: 0.0001 |
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- learning rate scheduler type: cosine |
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- Warmup ratio: 0.1 |
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- cutoff len (i.e. context length): 8192 |
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- global batch size: 2 |
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<br /> |
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# Usage |
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## Chat Template |
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The instruction-tuned models use a chat template that must be adhered to for conversational use. |
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The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. |
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Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: |
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```py |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import transformers |
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import torch |
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model_id = "ycjcl868/Gemma-1.1-7B-Chinese-Chat" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_id, |
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device_map="auto", |
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torch_dtype="auto", |
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) |
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messages = [ |
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{ "role": "user", "content": "写一首诗" }, |
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] |
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input_ids = tokenizer.apply_chat_template( |
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messages, add_generation_prompt=True, return_tensors="pt" |
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).to(model.device) |
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outputs = model.generate( |
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input_ids, |
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max_new_tokens=8192, |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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) |
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response = outputs[0][input_ids.shape[-1]:] |
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print(tokenizer.decode(response, skip_special_tokens=True)) |
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# 春风拂柳，春雨轻轻， |
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# 花香满园，鸟鸣清脆。 |
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# 一朵红梅，红彤彤的， |
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# 美不胜收，美不胜收。 |
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``` |
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# Examples |
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``` |
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User: 参加四年级数学竞赛同学中，男同学获奖人数比女同学多2人，女同学获奖人数比男同学人数的一半多2人，男女同学各有多少人获奖? |
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Assistant: |
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设男同学的获奖人数 |
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为x，则女同学的获奖人数为y，所以有以下两式： |
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x=y+2 |
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y=2(x+2) |
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将第一式代入第二式中，得： |
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y=2(y+2)+2 |
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y=2y+4+2 |
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y=2y+6 |
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2y=y+6 |
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y=6 |
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因此，女同学获奖人数为6人，男同学获奖人数为8人。 |
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``` |
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``` |
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User: 我的蓝牙耳机坏了，我应该去看耳鼻喉科还是牙科？ |
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Assistant: 蓝牙耳机是属于电子产品的，而不是耳鼻喉科或牙科的范畴。如果你怀疑耳机有故障，建议去电子产品维修店或者原厂维修中心进行检查。如果需要新的耳机，你也可以去电器商店或电子产品市场购买。 |
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``` |
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``` |
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User: 来几句成语 |
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Assistant: “一日三尺”是指过时间一切都会改变。 |
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``` |
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``` |
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User: 列出5种可以改善睡眠质量的方法 |
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Assistant: |
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1. 保持规律的睡眠时间表 |
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2. 避免下午摄入咖啡因 |
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3. 避免睡前摄入酒精 |
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4. 避免睡前摄入高糖饮食 |
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5. 每天至少进行30分钟的运动 |
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``` |
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``` |
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User: 回答一个开放性的问题：人类存在的意义是什么？ |
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Assistant: 人类存在的意义是一个复杂的哲学和哲学问题，因为人类存在于自然界中，我们作为人类有责任和义务来保护地球和生物，而我们作为人类也有责任来发展自己的精神和思想，来创造更好的未来。 |
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我从哲学角度来看，人类存在的意义是通过我们自己的思想和行动，来实现我们对精神和精神的追求。通过我们自己的努力和创造力，我们可以创造出更多的艺术、文化和思想，来促进人类的进步和发展。 |
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``` |
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``` |
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User: 解释以下缩写： JSON、CEO、PDF、HTML、NASA |
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Assistant: |
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JSON（JavaScript Object Notation）：JavaScript对象的表示法，用来在JavaScript和许多其他编程语言之间传递数据。 |
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CEO（Chief Executive Officer）：首席执行官，指企业的高级管理人员，通常是指企业的主席或总裁。 |
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PDF（Portable Document Format）：可移植文档格式，是一种电子文档格式，可以确保文档在不同的操作系统���设备上都能以一致的方式呈现。 |
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HTML（HyperText Markup Language）：超文本标记语言，是网页内容的标记语言，用来定义网页的结构和内容。 |
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NASA（National Aeronautics and Space Administration）：美国国家航空航天局，是美国政府的宇航机构，负责美国在太空和航空方面的研究和发展。 |
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``` |
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## Function call |
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**User**: |
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```` |
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以下是您可以使用的工具列表: |
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```python |
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def internet_search(query: str): |
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\"\"\" |
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Returns a list of relevant document snippets for a textual query retrieved from the internet |
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Args: |
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query (str): Query to search the internet with |
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\"\"\" |
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pass |
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``` |
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```python |
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def directly_answer(): |
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\"\"\" |
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Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history |
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\"\"\" |
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pass |
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``` |
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写 'Action:'，后跟要调用的 JSON 中的操作列表，例如. |
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Action: |
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```json |
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[ |
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{ |
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"tool_name": "tool name (one of [internet_search, directly_answer])", |
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"parameters": "the input to the tool" |
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} |
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] |
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``` |
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帮我找到今天的新闻有哪些: |
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```` |
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**Response**: |
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``` |
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Action: |
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[ |
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{ |
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"tool_name": "internet_search", |
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"parameters": "今天有哪些新闻" |
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} |
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] |
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``` |
