Update README.md

README.md

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----
-base_model: unsloth/Llama-3.2-3B-Instruct
-language:
-- en
-license: apache-2.0
-tags:
-- text-generation-inference
-- transformers
-- unsloth
-- llama
-- trl
----
-
-# Uploaded  model
-
-- **Developed by:** Jr23xd23
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
+Math_Arabic_Llama-3.2-3B-Instruct
+Model Description
+Math_Arabic_Llama-3.2-3B-Instruct is a fine-tuned version of the Llama-3.2-3B-Instruct model, specifically optimized for solving mathematical problems in Arabic. This model leverages the power of the Arabic LLaMA Math Dataset to provide accurate and contextually relevant solutions to a wide range of mathematical queries in the Arabic language.
+Key Features
 
+Specialized in Arabic mathematical problem-solving
+Covers a broad spectrum of mathematical topics
+Ideal for educational applications and Arabic-language tutoring systems
+Built on the robust Llama-3.2-3B-Instruct architecture
+
+Model Details
+
+Architecture: Transformer-based language model
+Task: Text Generation (Instruction Following)
+Language: Arabic
+Base Model: meta-llama/Llama-3.2-3B-Instruct
+Dataset: Arabic LLaMA Math Dataset
+Number of Parameters: 3 billion
+Fine-tuned by: Jr23xd23
+
+Training Data
+The model was fine-tuned on the Arabic LLaMA Math Dataset, which comprises 12,496 diverse mathematical examples covering:
+
+Basic Arithmetic
+Algebra
+Geometry
+Probability
+Combinatorics
+
+Each example in the dataset consists of:
+
+An Instruction: The problem statement in Arabic
+A Solution: The corresponding answer in Arabic
+
+Intended Use
+Primary Use Cases
+
+Solving mathematical problems in Arabic
+Powering educational applications for Arabic-speaking students
+Enhancing Arabic-language tutoring systems
+Facilitating mathematical reasoning tasks in Arabic
+
+Usage Example
+Here's how you can use the model with the Hugging Face Transformers library:
+pythonCopyfrom transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("Jr23xd23/Math_Arabic_Llama-3.2-3B-Instruct")
+model = AutoModelForCausalLM.from_pretrained("Jr23xd23/Math_Arabic_Llama-3.2-3B-Instruct")
+
+# Example: Solving a math problem in Arabic
+input_text = "ما هو مجموع الزوايا في مثلث؟"  # What is the sum of angles in a triangle?
+inputs = tokenizer(input_text, return_tensors="pt")
+output = model.generate(**inputs, max_length=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+Limitations
+
+The model is optimized for mathematical tasks in Arabic and may not perform well on general language tasks.
+Performance may decrease for extremely complex mathematical problems that fall outside the scope of the training dataset.
+The model's responses should be verified for critical applications.
+
+Ethical Considerations
+
+Users should be aware of potential biases in the training data.
+The model should not be used as the sole decision-maker in high-stakes scenarios.
+Implement appropriate safeguards when deploying this model in educational settings.
+
+License
+This model is licensed under the Apache 2.0 License.
+Citation
+If you use this model in your research or projects, please use the following citation:
+bibtexCopy@model{Math_Arabic_Llama_3.2_3B_Instruct,
+  title={Math_Arabic_Llama-3.2-3B-Instruct},
+  author={Jr23xd23},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/Jr23xd23/Math_Arabic_Llama-3.2-3B-Instruct},
+}
+Acknowledgements
+We extend our gratitude to the creators of the Arabic LLaMA Math Dataset for providing an invaluable resource that made this fine-tuning possible.