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README.md

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-license: apache-2.0
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+---
+license: apache-2.0 # Or your chosen open-source license (e.g., mit, cc-by-sa-4.0)
+---
+
+# [ALIF Base 100M]
+
+**[ALIF Base 100M]** is an Urdu generative language model from the **ALIF الف** series (a Final Year Project at Habib University), developed by **Orature AI**. This model is a [decoder-only Transformer / Naive GPT-2 based] architecture, specifically pretrained for the Urdu language.
+
+## Model Details
+
+*   **Developed by:** Orature AI (S.M Ali Naqvi, Zainab Haider, Haya Fatima, Ali M Asad, Hammad Sajid)
+<!-- *   **Supervised by:** Dr. Abdul Samad (Habib University) -->
+*   **Model type:** Decoder-only Transformer, GPT-like
+*   **Variant:** ALIF-Base-100M
+*   **Language(s) (NLP):** Urdu (ur)
+*   **License:** Apache 2.0
+<!-- *   **Finetuned from model (if applicable):** [e.g., `OratureAI/ALIF-Base-1B`] -->
+<!-- *   **Related Models:** Other models in the ALIF الف series by Orature AI. -->
+<!-- *   **Project Repository/Paper:** [Link to ALIF GitHub Repo or Paper arXiv/Website] -->
+* **Architecture:** Transformer (GPT-Based)
+* **Framework:** PyTorch
+* **Tokeniezer:** SentencePiece Custom Tokenizer
+* **Hyperparameters:**:
+    *   **Vocabulary Size:** 20000
+    *   **Embedding Size:** 768
+    *   **Attention Heads:** 12
+    *   **Layers:** 12 
+
+## How to Get Started with the Model
+
+First you will need to download the modeling_gpt.py file from the repo. Once that's been done, you can define another file and use the following code to generate text from the model:
+
+```python
+from modeling_gpt import GPTLanguageModel
+from transformers import AutoTokenizer
+import torch
+
+model_name = "OratureAI/[MODEL_NAME_ON_HF_HUB]" # e.g., OratureAI/ALIF-Instruct-1B
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# For text generation
+prompt_urdu = "ایک دفعہ کا ذکر ہے کہ " # "Once upon a time, "
+inputs = tokenizer.encode(prompt_urdu)
+inputs_tensor = torch.tensor(inputs).unsqueeze(0)  # Add batch dimension
+
+# Generate text
+outputs = model.generate(inputs_tensor, max_new_tokens=128, temperature=0.7)
+outputs_tensor = torch.tensor(encoded).unsqueeze(0)
+generated_text = tokenizer.decode(outputs_tensor[0].squeeze().tolist())
+
+print(f"Prompt: {prompt_urdu}")
+print(f"Generated Text: {generated_text}")
+```
+
+## Model Description
+
+ALIF Base 100M is designed to generate coherent and contextually relevant Urdu text. It leverages a custom Urdu tokenizer trained on the ALIF-Urdu-Corpus and was pretrained on a large corpus of diverse Urdu text.
+
+**Key Features:**
+*   Optimized for Urdu language nuances.
+*   Strong foundational capabilities for further fine-tuning (for base models)
+*   Capable of generating next tokens in a sequence, making it suitable for various text generation tasks.
+*   Part of a series aiming to provide efficient and accessible SLMs for Urdu.
+
+## Intended Uses & Limitations
+
+**Intended Uses:**
+*   **Text Generation:** Creative writing, content generation, story completion in Urdu.
+*   **Research:** Base for further research in Urdu NLP, low-resource language modeling.
+*   **Fine-tuning:** Can be fine-tuned for specific downstream tasks like sentiment analysis, summarization, or domain-specific chatbots in Urdu.
+*   **Educational Purposes:** Understanding SLM behavior for Urdu.
+*   **(For Instruct Models):** Conversational AI, Q&A, task completion in Urdu.
+
+**Limitations:**
+*   The model is primarily trained on Urdu and may not perform well on other languages or code-switched text unless specifically designed for it (e.g., an Ur-En variant).
+*   As a base generative model (especially for non-instruct versions), it may generate plausible-sounding but incorrect or nonsensical information (hallucinations).
+*   The model may reflect biases present in the training data. The ALIF-Urdu-Corpus was curated from diverse sources, but biases (e.g., societal, gender, regional) may still exist.
+*   Performance on highly specific or technical domains may be limited without further fine-tuning.
+*   The model does not have real-time knowledge and its information is limited to its training data.
+*   Safety: While efforts are made to curate data, the model might generate offensive, harmful, or inappropriate content. Users should implement appropriate safeguards for downstream applications.
+
+**Out-of-Scope Uses:**
+*   Generating high-stakes advice (medical, legal, financial) without human oversight.
+*   Impersonation or generating misleading information.
+*   Applications that could lead to harm or discrimination.
+*   Complex scientific, technical, mathematical, or legal reasoning without further fine-tuning.
+*   Any use that violates ethical guidelines or legal standards.