Update README.md

README.md

@@ -103,6 +103,7 @@ tags:
 - audio
 - automatic-speech-recognition
 - hf-asr-leaderboard
+- open4bits
 widget:
 - example_title: Librispeech sample 1
   src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
@@ -159,13 +160,64 @@ base_model:
 - openai/whisper-base
 ---
 
-# Whisper Base FP16 (SafeTensors)
+# Open4bits / Whisper Base FP16
 
-This repository contains **OpenAI Whisper Base** converted to **float16**
-and stored in **SafeTensors** format.
+This repository provides the **Whisper Base model converted to FP16 (float16) precision**, published by Open4bits to enable more efficient inference while maintaining transcription quality.
 
-## Details
-- Parameters: ~74M
-- Precision: float16
-- Weight tying preserved
-- Transformers compatible
+The underlying Whisper model and architecture are **owned by OpenAI**. This repository contains only a precision-converted version of the original model weights.
+
+The model is designed for multilingual speech-to-text tasks and can be used in research, experimentation, and production ASR pipelines.
+
+---
+
+## Model Overview
+
+Whisper is a sequence-to-sequence transformer model developed by OpenAI for automatic speech recognition and speech translation.  
+This release uses the **Base** variant and preserves the original architecture while reducing memory usage through FP16 precision.
+
+---
+
+## Model Details
+
+- **Architecture:** Whisper Base  
+- **Parameters:** ~74 million  
+- **Precision:** float16 (FP16)  
+- **Task:** Automatic Speech Recognition (ASR)  
+- **Languages:** Multilingual  
+- **Weight tying:** Preserved  
+- **Compatibility:** Hugging Face Transformers, PyTorch  
+
+This conversion improves inference speed and lowers VRAM requirements compared to FP32 versions, making it suitable for deployment on consumer and server-grade GPUs.
+
+---
+
+## Intended Use
+
+This model is intended for:
+- Speech-to-text transcription
+- Multilingual ASR applications
+- Research and benchmarking
+- Efficient inference in low-memory environments
+
+---
+
+## Limitations
+
+* Performance depends on audio quality, language, and accent
+* Inherits known limitations of the Whisper Base architecture
+* Not fine-tuned for domain-specific or highly noisy audio
+
+---
+
+## License
+
+This model is released under the **Apache License 2.0**.
+The original Whisper model and associated intellectual property are owned by OpenAI.
+
+---
+
+## Support
+
+If you find this model useful, please consider supporting the project.
+Your support helps us continue releasing and maintaining high-quality open models.
+Support us with a heart.