bilibot
B
Bilibot
Overview :
bilibot is a local chatbot trained on Bilibili user comments, supporting both text chat and voice dialogue. It uses Qwen1.5-32B-Chat as the base model and is further fine-tuned with Apple's mlx-lm LORA project. The voice generation part is based on the GPT-SoVITS project, utilizing the Paimon voice model. This chatbot can quickly generate conversational content and is suitable for scenarios requiring intelligent dialogue systems.
Target Users :
bilibot is suitable for developers and enterprises that need intelligent chat systems, especially those who want to provide personalized services and enhance user experience. This includes fields like customer service, online education, and entertainment interaction.
Total Visits: 474.6M
Top Region: US(19.34%)
Website Views : 65.7K
Use Cases
As an online customer service representative, providing 24/7 consultation services.
As a smart tutor on an educational platform, assisting with teaching and answering questions.
Providing role-playing and interactive experiences in entertainment applications.
Features
Supports text chat to meet basic communication needs.
Can generate voice dialogues in response to given questions, providing richer interaction experiences.
Utilizes Qwen1.5-32B-Chat as the base model, ensuring high-quality and diverse dialogues.
Fine-tuned through training, making the chatbot closer to Bilibili users' communication habits.
Employs GPT-SoVITS for voice generation, enhancing the naturalness of voice interaction.
Model quantization compression accelerates content generation speed, improving user experience.
Supports customized questions and answers, adding flexibility and applicability to the chatbot.
How to Use
First, visit the bilibot GitHub page and clone or download the project code.
Configure the Python environment according to the project documentation. Anaconda is recommended.
Run the model fine-tuning training and inference testing. Merge the fine-tuned adapters file with the base model.
Use quantization acceleration tools to compress the model to improve generation speed.
Run the dialogue test script to test the chatbot's dialogue functionality.
Configure the voice generation service as needed to generate voice dialogues.
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