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app.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Load the fine-tuned model and tokenizer
+model_path = "BoburAmirov/test-llama-uz"  # Adjust this to the path where your fine-tuned model is saved
+
+model = AutoModelForCausalLM.from_pretrained(model_path, device_map='auto')
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# Ensure the tokenizer settings match those used during training
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+# Set the model to evaluation mode
+model.eval()
+
+def generate_text(input_prompt):
+    # Tokenize the input
+    input_ids = tokenizer(input_prompt, return_tensors="pt")
+
+    # Generate text
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            max_length=400,  # Adjust max_length as needed
+            num_return_sequences=1,
+            temperature=0.7,  # Control randomness
+            top_p=0.9,  # Control diversity
+            top_k=50,  # Control diversity
+        )
+
+    # Decode the generated text
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs="text",
+    title="Text Generation with LLaMA",
+    description="Generate text using a fine-tuned LLaMA model."
+)
+
+if __name__ == "__main__":
+    iface.launch(server_name="0.0.0.0", server_port=7860)

requirements.txt

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+torch
+transformers
+gradio