nateshmbhat/model-isha-qa
The nateshmbhat/model-isha-qa is a 13 billion parameter language model, fine-tuned by nateshmbhat from a StableBeluga2 base, which itself is a finetuned Llama2-13B. This model is specifically trained for question-answering tasks within a call center context, leveraging a specialized dataset. It is optimized for generating relevant and accurate responses to user queries, making it suitable for automated customer support applications.
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Model Overview
nateshmbhat/model-isha-qa is a 13 billion parameter language model developed by nateshmbhat. It is built upon a fine-tuned version of StableBeluga2, which originates from the Llama2-13B architecture. The model has been specifically trained for question-answering (QA) tasks, particularly in a call center environment.
Key Capabilities
- Specialized QA: Fine-tuned on the
nateshmbhat/isha-qa-textdataset, making it proficient in handling specific types of queries relevant to call center operations. - Efficient Training: Utilizes 4-bit quantization and a learning rate of 2e-4, trained for 3 epochs with a batch size of 2, indicating an optimized training process.
- Context Length: Capable of processing inputs up to 2048 tokens, with potential for higher context lengths.
Good For
- Automated Call Center Support: Ideal for deploying as an AI assistant to answer common customer questions.
- Information Retrieval: Excels at extracting and synthesizing information to provide direct answers based on its training data.
- Domain-Specific QA: Particularly effective for question-answering within the domain covered by the
isha-qa-textdataset.