Update README.md

README.md

@@ -27,6 +27,10 @@ This model is designed for NLP tasks involving Quranic text in Bahasa Indonesia,
 
 ## Uses
 
+### Direct Use
+
+This model can be used for applications requiring the understanding, summarization, or retrieval of Quranic translations and tafsir in Bahasa Indonesia.
+
 ### Downstream Use
 
 It is suitable for fine-tuning on tasks such as:
@@ -34,7 +38,6 @@ It is suitable for fine-tuning on tasks such as:
 - Question answering systems related to Islamic knowledge
 - Educational tools for learning Quranic content in Indonesian
 
-
 ### Biases
 - The model inherits any biases present in the dataset, which is specific to Islamic translations and tafsir in Bahasa Indonesia.
 
@@ -47,10 +50,36 @@ It is suitable for fine-tuning on tasks such as:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF")
-model = AutoModelForCausalLM.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran-GGUF")
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran")
+model = AutoModelForCausalLM.from_pretrained("Ellbendls/Qwen-2.5-3b-Quran")
+
+# Move the model to GPU
+model.to("cuda")
+
+# Define the input message
+messages = [
+    {
+        "role": "user", 
+        "content": "Tafsirkan ayat ini اِهْدِنَا الصِّرَاطَ الْمُسْتَقِيْمَۙ"
+    }
+]
+
+# Generate the prompt using the tokenizer
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, 
+                                       add_generation_prompt=True)
+
+# Tokenize the prompt and move inputs to GPU
+inputs = tokenizer(prompt, return_tensors='pt', padding=True, 
+                   truncation=True).to("cuda")
+
+# Generate the output using the model
+outputs = model.generate(**inputs, max_length=150, 
+                         num_return_sequences=1)
+
+# Decode the output
+text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-input_text = "Apa tafsir dari Surat Al-Fatihah ayat 1?"
-inputs = tokenizer(input_text, return_tensors="pt")
-outputs = model.generate(**inputs)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+# Print the result
+print(text.split("assistant")[1])
+```