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Gemma 2 9B Chinese Chat
Overview :
Gemma-2-9B-Chinese-Chat is an instruction-tuned language model based on google/gemma-2-9b-it, specifically designed for Chinese and English users. It boasts capabilities such as role-playing and tool usage. Fine-tuned through the ORPO algorithm, the model significantly enhances the accuracy of responses to Chinese queries, minimizes issues with mixed Chinese and English usage, and excels in role-playing, tool usage, and mathematical calculations.
Target Users :
This product is suitable for users who need to interact in both Chinese and English, including but not limited to AI researchers, developers, language learners, and ordinary users interested in AI conversational capabilities. It caters to multi-lingual communication needs by providing rich conversational features and accurate language processing.
Use Cases
A user requests a poem about machine learning, and the model successfully creates and returns the result.
A user engages in a conversation as Newton, and the model responds in the language style of the 17th century.
A user requests to send an email, and the model simulates the email sending process through its tool usage functionality.
Features
Supports role-playing, enabling conversations in a specific character's style.
Possesses tool usage capabilities, allowing it to execute tasks like searching and sending emails.
Optimizes both Chinese and English conversational abilities, reducing language mixing problems.
Provides the ability to solve mathematical problems.
Supports text generation, capable of creating poems, stories, etc.
Offers safe and appropriate conversations, avoiding sensitive or inappropriate content.
How to Use
1. Visit the product page and download the required model files.
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2. Configure your environment and install necessary libraries according to the provided Python script examples.
3. Use the script to download the model and deploy it locally.
4. Interact with the model by writing dialogue templates or directly inputting commands.
5. Adjust model parameters such as temperature and top_p as needed to achieve different output styles.
6. Analyze the model's output results and further develop or utilize it based on your application scenario.
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