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Model Card: Llama-7b with LoRA Fine-tuning on QACR data

Model Overview

  • Model Name: Llama-7b
  • Model Architecture: Transformer-based Language Model
  • Fine-tuning Method: LoRA
  • Training Datasets:
    • Educational Question Generation Dataset (described in the dataset chart)
    • Alpaca GPT-4 french dataset (chat instruction task)
    • Dolly_fr dataset (chat instruction task)

Model Details

  • Base Model: decapoda-research/llama-7b-hf
  • Fine-tuning Approach: LoRA fine-tuning method, which combines pre-training on a large corpus with additional task-specific fine-tuning.
  • Training Objective: The model is trained to generate relevant and useful questions based on educational texts and to handle chat instruction tasks from the Alpaca GPT-4 and Dolly datasets.
  • Training Procedure: The base Llama-7b model is first pretrained on a large corpus to learn general language patterns and representations. It is then fine-tuned using a combination of the aforementioned datasets to specialize in educational question generation and chat instruction tasks.

Intended Use

  • Primary Task: Question generation for educational purposes and chat instruction tasks.
  • Potential Use Cases:
    • Automated question generation for educational platforms and tutoring systems.
    • Chat-based instruction and assistance in various domains.
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