vivekmdrift/maya-qwen-7b
The vivekmdrift/maya-qwen-7b is a 7.6 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-7B-Instruct. It is specifically optimized for customer support conversations, leveraging a 32768-token context length. This model is designed to provide helpful responses in customer service interactions.
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Model Overview
The vivekmdrift/maya-qwen-7b is a specialized large language model, fine-tuned from the robust Qwen/Qwen2.5-7B-Instruct base model. With 7.6 billion parameters and a substantial 32768-token context window, this model is engineered for specific conversational tasks.
Key Capabilities
- Customer Support Optimization: The primary focus of this model is to excel in customer support conversations, providing helpful and relevant responses.
- Instruction Following: Built upon an instruction-tuned base, it is designed to follow user prompts effectively within its specialized domain.
- ChatML Format: The model is trained to understand and generate responses in the ChatML format, facilitating easy integration into chat-based applications.
Training Details
This model was fine-tuned using the LoRA (Low-Rank Adaptation) method with a rank of 16, leveraging Unsloth and TRL SFTTrainer for efficient adaptation. The training specifically targeted customer support dialogues, making it a suitable choice for automating or assisting in customer service roles.
Good For
- Automated Customer Service: Ideal for chatbots or virtual assistants handling customer inquiries.
- Support Agent Assistance: Can be used to generate draft responses or provide information to human customer support agents.
- Conversational AI: Suitable for applications requiring focused, helpful dialogue in a customer interaction context.