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+# PruneSLU-30M: Enhanced Model for On-Device Spoken Language Understanding
+
+**PruneSLU-30M** is an enhanced version of the [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) model, designed for robust Spoken Language Understanding (SLU) tasks. This model strikes a balance between performance and efficiency, making it suitable for more demanding on-device applications.
+
+### Model Overview
+
+- **Base Model:** [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en)
+- **Task:** Spoken Language Understanding (SLU)
+- **Dataset:** Fine-tuned on the [STOP dataset](https://github.com/facebookresearch/fairseq/tree/main/examples/audio_nlp/nlu)
+- **Pruning Techniques:** Employs vocabulary pruning and layer-wise structural pruning, followed by retraining to create a model that is both efficient and high-performing.
+
+### Key Features
+
+- **Optimized Size:** PruneSLU-30M contains 30 million parameters, offering a higher capacity for SLU tasks while remaining suitable for on-device deployment.
+- **Improved Performance:** This model is designed to handle more complex SLU tasks, providing enhanced accuracy and robustness compared to lighter models.
+- **Seamless Integration:** The model can be easily accessed and utilized through the Hugging Face Transformers library.
+
+### Usage
+
+To load the PruneSLU-30M model in Hugging Face, use the following code:
+
+```python
+from transformers import WhisperForConditionalGeneration
+
+model = WhisperForConditionalGeneration.from_pretrained("kodiak619/PruneSLU-30M")
+```
+
+### Applications
+PruneSLU-30M is ideal for applications requiring a balance between computational efficiency and performance, such as voice-enabled AI systems, smart assistants, and SLU tasks in moderately resource-constrained environments.