Update README.md

README.md

@@ -7,13 +7,10 @@ tags: []
 
 This model is a quantized version of openai/whisper-large-v3, optimized for more efficient use while maintaining performance.
 
-
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
 This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 
 - **Developed by:** alicekyting (based on OpenAI's Whisper model)
@@ -25,6 +22,9 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 This model can be used for automatic speech recognition (ASR) tasks, including transcription and translation. 
 It's particularly useful in scenarios where computational efficiency is important, as it has been quantized to 4-bit precision.
 
+## Hardware Requirements
+It is recommended to use this model on a device with a compatible GPU.
+
 ## Bias, Risks, and Limitations
 
 This model inherits any biases, risks, and limitations present in the original openai/whisper-large-v3 model. 
@@ -43,18 +43,22 @@ Use the following code to load and use the model:
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
 import torch
 
+# Load the model
 model = AutoModelForSpeechSeq2Seq.from_pretrained(
-    "alicekyting/whisper-large-v3-4bit-model",
+    "alicekyting/whisper-large-v3-4bit",
     device_map="auto",
     torch_dtype=torch.float16,
+    use_safetensors=True,
 )
 
-processor = AutoProcessor.from_pretrained("alicekyting/whisper-large-v3-4bit-model")
+# Load the processor
+processor = AutoProcessor.from_pretrained("alicekyting/whisper-large-v3-4bit")
 
 pipe = pipeline(
     "automatic-speech-recognition",
     model=model,
     tokenizer=processor.tokenizer,
     feature_extractor=processor.feature_extractor,
-    torch_dtype=torch_dtype,
+    torch_dtype=torch.float16,
+    device_map="auto"
 )