language:
- en
- fr
license: mit
datasets:
- UMA-IA/UMA_Dataset_Engine_Aero_LLM
base_model: mistralai/Mistral-7B-v0.1
tags:
- aerospace
- aeronautics
- engineering
- technical-QA
pipeline_tag: text-generation
Model Details
Model Name: UMA-IA/LLM_Engine_Finetuned_Aero Authors:
- Youri LALAIN, Engineering student at French Engineering School ECE
- Lilian RAGE, Engineering student at French Engineering School ECE
Base Model: Mistral-7B-v0.1
Fine-tuned Dataset: UMA-IA/UMA_Dataset_Engine_Aero_LLM
License: Apache 2.0
Model Description
Mistral-7B Fine-tuné sur les moteurs aérospatiaux
LLM_Engine_Finetuned_Aero is a specialized fine-tuned version of Mistral-7B designed to provide accurate and detailed answers to technical questions related to aerospace and aeronautical engines. The model leverages the UMA-IA/UMA_Dataset_Engine_Aero_LLM to enhance its understanding of complex engineering principles, propulsion systems, and aerospace technologies.
Capabilities
- Technical Q&A on aerospace and aeronautical engines
- Analysis and explanations of propulsion system components
- Assistance in understanding aerospace engineering concepts
Use Cases
- Aerospace research and engineering support
- Educational purposes for students and professionals
- Assisting in aerospace-related R&D projects
Training Details
This model was fine-tuned on UMA-IA/UMA_Dataset_Engine_Aero_LLM, a curated dataset focusing on aerospace engines, propulsion systems, and general aeronautical engineering. The fine-tuning process was performed using supervised learning to adapt Mistral-7B to technical discussions.
How to Use
You can load the model using Hugging Face's transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "UMA-IA/LLM_Engine_Finetuned_Aero"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
input_text = "Explain the working principle of a turbofan engine."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))