Update README.md

README.md

@@ -1,22 +1,73 @@
+---
+base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- trl
+license: apache-2.0
+language:
+- en
+- ar
+datasets:
+- Abdulrhman37/metallurgy-qa
+pipeline_tag: text2text-generation
+---
+
+# Fine-Tuned Llama Model for Metallurgy and Materials Science
+
+- **Developed by:** Abdulrhman37  
+- **License:** [Apache-2.0](https://opensource.org/licenses/Apache-2.0)  
+- **Base Model:** [unsloth/meta-llama-3.1-8b-bnb-4bit](https://huggingface.co/unsloth/meta-llama-3.1-8b-bnb-4bit)  
+
+This fine-tuned Llama model specializes in **metallurgy, materials science, and engineering**. It has been enhanced to provide precise and detailed responses to technical queries, making it a valuable tool for professionals, researchers, and enthusiasts in the field.
+
 ---
-base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
-license: apache-2.0
-language:
-- en
+
+## 🛠️ Training Details
+
+This model was fine-tuned with:
+- **[Unsloth](https://github.com/unslothai/unsloth):** Enabled 2x faster training using efficient parameter optimization.  
+- **[Hugging Face TRL](https://huggingface.co/transformers/main_classes/trainer.html):** Used for advanced fine-tuning and training capabilities.
+
+Fine-tuning focused on enhancing domain-specific knowledge using a dataset curated from various metallurgical research and practical case studies.
+
 ---
 
-# Uploaded  model
+## 🔑 Features
+- Supports **text generation** with scientific and technical insights.
+- Provides **domain-specific reasoning** with references to key metallurgical principles and mechanisms.
+- Built for fast inference with **bnb-4bit quantization** for optimized performance.
+
+---
+
+## 🌟 Example Use Cases
+- **Material property analysis** (e.g., "How does adding rare earth elements affect magnesium alloys?").  
+- **Failure mechanism exploration** (e.g., "What causes porosity in gas metal arc welding?").  
+- **Corrosion prevention methods** (e.g., "How does cathodic protection work in marine environments?").
+
+---
+
+## 📦 How to Use
+
+You can load the model using the `transformers` library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Abdulrhman37/metallurgy-llama")
+model = AutoModelForCausalLM.from_pretrained("Abdulrhman37/metallurgy-llama")
+
+# Example Query
+prompt = "Explain the role of manganese in Mg-Al-Mn systems."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=150)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(response)
 
-- **Developed by:** Abdulrhman37
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
 
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)