Update README.md

README.md

@@ -1,19 +1,19 @@
----
-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
----
+---
+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
 
@@ -51,22 +51,15 @@ Fine-tuning focused on enhancing domain-specific knowledge using a dataset curat
 
 ## 📦 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)
+follow this [notebook](https://colab.research.google.com/drive/1pRNcAtybNF6w6mE1ZReFwfrIujZ5_t4S#scrollTo=wk4fCWOl0Ocd) for help to use the model
 
-print(response)
+## 📧 Contact
+For any inquiries, feedback, or collaboration opportunities, feel free to reach out:
 
+- Email: [abdodebo3@gmail.com](mailto:abdodebo3@gmail.com)
+- [LinkedIn](https://www.linkedin.com/in/abdulrahman-eldeeb-8b4621253/)
+- [GitHub](https://github.com/AdbulrhmanEldeeb)
+- Phone: +20 1026821545
 
 This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.