Update README.md

README.md

@@ -1,5 +1,115 @@
 ---
+language:
+- en
+- as
+- bn
+- brx
+- doi
+- gu
+- hi
+- kn
+- ks
+- kok
+- mai
+- ml
+- mni
+- mr
+- ne
+- or
+- pa
+- sa
+- sat
+- sd
+- ta
+- te
+- ur
+- bho
+- bhb
+library_name: transformers
+pipeline_tag: automatic-speech-recognition
+base_model:
+- bodhan-ai/indic-transcribe-core
+tags:
+- automatic-speech-recognition
+- speech
+- audio
+- asr
+- multilingual
+- indic
+- code-switching
+- code-mixing
+- language-identification
+- canary
+- fastconformer
+- quantized
+- int8
+- bitsandbytes
 license: other
-license_name: other
-license_link: LICENSE
 ---
+
+# Indic-Transcribe-Core 8-bit
+
+An **8-bit quantized version** of [Bodhan AI's Indic-Transcribe-Core](https://huggingface.co/bodhan-ai/indic-transcribe-core), created using **BitsAndBytes** quantization.
+
+The model retains the capabilities of the original multilingual ASR model while significantly reducing GPU memory requirements, making it more suitable for resource-constrained inference and serving multiple users.
+
+## Key Features
+
+* 🎙️ Multilingual Automatic Speech Recognition
+* 🇮🇳  Supports Indian languages
+* ⚡ 8-bit quantization using **BitsAndBytes**
+* 💾 Reduced GPU VRAM usage
+* 🔌 Designed for efficient inference and deployment
+
+## VRAM Usage
+
+| Model                            | Approx. GPU VRAM |
+| -------------------------------- | ---------------: |
+| Original `indic-transcribe-core` |            ~5 GB |
+| **This 8-bit model**             |      **~2.5 GB** |
+| Reduction                        |         **~50%** |
+
+Actual memory usage may vary depending on GPU, batch size, audio duration, and inference configuration.
+
+## Base Model
+
+This model is derived from:
+
+**[bodhan-ai/indic-transcribe-core](https://huggingface.co/bodhan-ai/indic-transcribe-core)**
+
+Please refer to the original model card for the architecture, training details, supported languages, benchmarks, limitations, and licensing information.
+
+## Quantization
+
+The model was quantized to **8-bit using BitsAndBytes**.
+
+The primary goal of this quantized release is to reduce GPU memory consumption while maintaining the capabilities of the original model.
+
+
+
+## Inference
+
+For a complete inference example, see the
+[Inference Notebook](./transcribe-8bit-quant.ipynb).
+
+
+## Intended Use
+
+Suitable for:
+
+* Indian-language speech-to-text
+* Voice applications
+* Multilingual transcription
+* Low-VRAM GPU deployment
+* Multi-user ASR serving
+* On-premise and edge-oriented inference
+
+## Attribution
+
+This is a quantized derivative of **Bodhan AI's Indic-Transcribe-Core**.
+Please retain the original model's attribution and comply with its license and usage requirements.
+
+## Disclaimer
+
+This repository contains an **8-bit quantized version** of the original model.
+Performance, accuracy, memory usage, and compatibility may vary depending on the inference environment.