Update README.md

README.md

@@ -1,11 +1,48 @@
----
-license: apache-2.0
-datasets:
-- SpaceYL/Aerospatial_Dataset
-base_model:
-- mistralai/Mistral-7B-v0.1
-pipeline_tag: text-generation
----
+# Model Card: Engine_Finetuned_V2.2
+
+## Model Details
+
+**Model Name:** Engine_Finetuned_V2.2  
+**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:** [SpaceYL/Aerospatial_Dataset](https://huggingface.co/datasets/SpaceYL/Aerospatial_Dataset)  
+**License:** Apache 2.0  
+
+## Model Description
+
 # Mistral-7B Fine-tuné sur les moteurs aérospatiaux
 
-Modèle fine-tuné pour répondre à des questions techniques sur les moteurs Vulcain 2 (spatial) et CFM56 (aéronautique).
+Engine_Finetuned_V2.2 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 SpaceYL/Aerospatial_Dataset 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 SpaceYL/Aerospatial_Dataset, 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 = "SpaceYL/Engine_Finetuned_V2.2"
+
+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))