README.md

metadata

library_name: Llama3-8b-FlyingManual-Tutor
tags:
  - llama3
  - flying-manual
  - ai-tutoring
  - llama-factory

Model Card for Llama3-8b-FlyingManual-Tutor

This model is a fine-tuned version of the Llama3-8b model, specifically trained on the FlyingManual dataset to serve as an AI tutor for aviation-related subjects. It is designed to provide guidance and nudge users when they answer questions incorrectly.

Model Details

Model Description

• Developed by: Canarie Teams
• Model type: Large Language Model (LLM) for AI Tutoring
• Language(s) (NLP): English (primary), potentially others depending on the FlyingManual dataset
• Finetuned from model: Llama3-8b by Meta AI

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "path/to/your/Llama3-8b-FlyingManual-Tutor"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage for tutoring
def tutor_interaction(question, user_answer):
    prompt = f"Question: {question}\nUser Answer: {user_answer}\nTutor Response:"
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=200)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("Tutor Response:")[-1].strip()

# Example
question = "What are the primary flight controls of an aircraft?"
user_answer = "Steering wheel and gas pedal"
tutor_feedback = tutor_interaction(question, user_answer)
print(tutor_feedback)

Training Details

Training Data

The model was fine-tuned on the FlyingManual dataset, augmented with:

• Sample Q&A pairs related to aviation topics
• Examples of constructive feedback and explanations
• Scenarios demonstrating correct and incorrect responses to aviation-related questions

Training Procedure

Preprocessing

• Conversion of training data into a dialogue format suitable for tutoring interactions
• Augmentation of data with tutoring-specific tokens or markers

Training Hyperparameters

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Evaluation

Testing Data, Factors & Metrics

Testing Data

A held-out portion of the FlyingManual dataset, supplemented with:

• A set of typical student questions and answers
• Scenarios designed to test the model's ability to provide constructive feedback

Metrics

• Human evaluation of tutoring quality (clarity, accuracy, helpfulness)
• Task-specific metrics (e.g., ability to correctly identify and address user mistakes)

Results

[Provide the evaluation results here]

Environmental Impact

• Hardware Type: 8 x NVIDIA A100 40GB GPUs

Model Card Authors

Canarie Teams

Model Card Contact

[Your contact information or a link to where people can reach out with questions]