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app.py

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+from unsloth import FastLanguageModel
+from transformers import TextStreamer
+import gradio as gr
+import torch
+
+# Model Configuration
+max_seq_length = 2048
+dtype = None
+load_in_4bit = True
+
+# Load the model and tokenizer
+model_name = "unsloth/Mistral-Nemo-Base-2407"
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=max_seq_length,
+    dtype=dtype,
+    load_in_4bit=load_in_4bit,
+)
+FastLanguageModel.for_inference(model)  # Optimize for inference
+
+# Define the Alpaca-style prompt
+alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+
+# Define the MCQ generation function
+def generate_mcq(passage):
+    instruction = "Generate a multiple-choice question (MCQ) based on the passage, provide options, and indicate the correct option."
+    input_text = f"Passage: {passage}"
+    prompt = alpaca_prompt.format(instruction, input_text, "")
+
+    # Tokenize input and generate output
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=max_seq_length).to("cuda")
+    outputs = model.generate(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        max_new_tokens=128,
+        pad_token_id=tokenizer.eos_token_id,
+        num_return_sequences=1,
+        do_sample=True,
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_mcq,
+    inputs=[gr.Textbox(label="Enter Passage", placeholder="Enter a passage to generate MCQs")],
+    outputs="text",
+    title="MCQ Generator with Mistral",
+    description="Generate multiple-choice questions using Mistral LLM. Enter a passage and get an MCQ with answer options."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch
+datasets
+unsloth
+transformers
+trl
+bert-score
+rouge-score
+tqdm