Update README.md

README.md

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 ## Note
-Introducing AgriQBot 🌾🤖: Embarking on the journey to cultivate knowledge in agriculture! 🚜🌱 Currently in its early testing phase, AgriQBot is a multilingual small language model dedicated to agriculture. 🌍🌾 As we harvest insights, the data generation phase is underway, and continuous improvement is the key. 🔄💡 The vision? Crafting a compact yet powerful model fueled by a high-quality dataset, with plans to fine-tune it for direct tasks in the future.
+Introducing AgriQBot 🌾🤖: Embarking on the journey to cultivate knowledge in agriculture! 🚜🌱 Currently in its early testing phase, AgriQBot is a multilingual small language model dedicated to agriculture. 🌍🌾 As we harvest insights, the data generation phase is underway, and continuous improvement is the key. 🔄💡 The vision? Crafting a compact yet powerful model fueled by a high-quality dataset, with plans to fine-tune it for direct tasks in the future.
+
+```python
+# Use a pipeline as a high-level helper
+
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", model="mrSoul7766/AgriQBot")
+
+# Example user query
+user_query = "How can I increase the yield of my potato crop?"
+
+# Generate response
+answer = pipe(f"answer: {user_query}", max_length=512)
+
+# Print the generated answer
+print(answer[0]['generated_text'])
+```
+### or
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/AgriQBot")
+model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/AgriQBot")
+
+# Set maximum generation length
+max_length = 512
+
+# Generate response with question as input
+input_ids = tokenizer.encode("answer: How can I increase the yield of my potato crop?", return_tensors="pt")
+output_ids = model.generate(input_ids, max_length=max_length)
+
+# Decode response
+response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print(response)
+```