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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.
Model Details
- Developed by: Orature AI (S.M Ali Naqvi, Hammad Sajid, Zainab Haider, Haya Fatima, Ali M Asad)
- 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
- Architecture: Transformer (GPT-Based)
- Framework: PyTorch
- Tokeniezer: SentencePiece Custom Tokenizer
- Hyperparameters::
- Vocabulary Size: 32000
- 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:
from modeling_gpt import GPTLanguageModel
from transformers import AutoTokenizer
import torch
model_name = "orature/ALIF-Base-100M"
model = GPTLanguageModel.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=64, temperature=0.7)
outputs_tensor = torch.tensor(outputs).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.
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, 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.