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---
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
```python
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
-----------------------------
## 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] |