README.md

---
widget:
- text: "Jens Peter Hansen kommer fra Danmark"
language:
- fr
tags:
- llama
license: other
base_model:
- decapoda-research/llama-7b-hf
---

# 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.