sharthakdey/interviewer-model
The sharthakdey/interviewer-model is a 7 billion parameter Mistral-based instruction-tuned causal language model developed by sharthakdey. Finetuned from unsloth/mistral-7b-instruct-v0.2-bnb-4bit, this model was trained using Unsloth and Huggingface's TRL library for accelerated finetuning. It is designed for conversational tasks, particularly those mimicking an interview scenario, leveraging its Mistral architecture for effective instruction following.
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Model Overview
The sharthakdey/interviewer-model is a 7 billion parameter instruction-tuned language model, developed by sharthakdey. It is built upon the Mistral architecture, specifically finetuned from the unsloth/mistral-7b-instruct-v0.2-bnb-4bit base model.
Key Characteristics
- Architecture: Mistral-based, leveraging the efficient design of the Mistral 7B model.
- Finetuning: The model was finetuned using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process.
- Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a context length of 4096 tokens.
Intended Use Cases
This model is primarily designed for conversational AI applications, particularly those requiring instruction-following capabilities in a dialogue format. Its finetuning process suggests an optimization for interactive scenarios, making it suitable for:
- Interview Simulation: Generating responses and questions in a simulated interview setting.
- Instruction Following: Executing specific commands or answering questions based on provided instructions.
- General Conversational AI: Engaging in coherent and contextually relevant dialogue.