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---
language:
- en
- fr
license: mit
datasets:
- UMA-IA/VELA-Engine-v1
base_model: mistralai/Mistral-7B-v0.1
tags:
- aerospace
- aeronautics
- engineering
- technical-QA
pipeline_tag: text-generation
---
## Model Details
**Model Name:** UMA-IA/CENTAURUS-Engine-v1
**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](https://huggingface.co/mistralai/Mistral-7B-v0.1)
**Fine-tuned Dataset:** [UMA-IA/VELA-Engine-v1](https://huggingface.co/datasets/UMA-IA/UMA_Dataset_Engine_Aero_LLM)
**License:** Apache 2.0
## Model Description
# Mistral-7B Fine-tuné sur les moteurs aérospatiaux
UMA-IA/CENTAURUS-Engine-v1 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/VELA-Engine-v1, 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:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "UMA-IA/CENTAURUS-Engine-v1"
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)) |