Add application file

app.py

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+import gradio as gr
+import torch
+from transformers import pipeline
+
+model_name = "eljanmahammadli/AzLlama-152M-Alpaca"
+model = pipeline("text-generation", model=model_name, torch_dtype=torch.float16)
+logo_path = "/Users/eljan/Documents/AzLlama/AzLlama-logo.webp"
+
+
+def get_prompt(question):
+    base_instruction = "Aşağıda tapşırığı təsvir edən təlimat və əlavə kontekst təmin edən giriş verilmiştir. Sorğunu uyğun şəkildə tamamlayan cavab yazın."
+    prompt = f"""{base_instruction}
+
+### Təlimat:
+{question}
+
+### Cavab:
+"""
+    return prompt
+
+
+def get_answer(llm_output):
+    return llm_output.split("### Cavab:")[1].strip()
+
+
+def answer_question(history, temperature, top_p, repetition_penalty, top_k, question):
+    model_params = {
+        "temperature": temperature,
+        "top_p": top_p,
+        "repetition_penalty": repetition_penalty,
+        "top_k": top_k,
+        "max_length": 512,  # Adjust based on your needs
+        "do_sample": True,
+    }
+    prompt = get_prompt(question)
+    llm_output = model(prompt, **model_params)[0]
+    answer = get_answer(llm_output["generated_text"])
+    divider = "\n\n" if history else ""
+    print(answer)
+    new_history = history + divider + f"USER: {question}\nASSISTANT: {answer}\n"
+    return new_history, ""  # Return updated history and clear the question input
+
+
+def send_action(_=None):
+    send_button.click()
+
+
+with gr.Blocks() as app:
+    gr.Markdown("# AzLlama-150M Chatbot\n\n")
+
+    with gr.Row():
+        with gr.Column(scale=0.2, min_width=200):
+            gr.Markdown("### Model Logo")
+            gr.Image(
+                value=logo_path,
+            )
+            # write info about the model
+            gr.Markdown(
+                "### Model Info\n"
+                "This model is a 150M paramater LLaMA2 model trained from scratch on Azerbaijani text. It can be used to generate text based on the given prompt. "
+            )
+        with gr.Column(scale=0.6):
+            gr.Markdown("### Chat with the Assistant")
+            history = gr.Textbox(
+                label="Chat History", value="", lines=20, interactive=False
+            )
+            question = gr.Textbox(
+                label="Your question",
+                placeholder="Type your question and press enter",
+            )
+            send_button = gr.Button("Send")
+        with gr.Column(scale=0.2, min_width=200):
+            gr.Markdown("### Model Settings")
+            temperature = gr.Slider(
+                minimum=0.1, maximum=1.0, value=0.9, label="Temperature"
+            )
+            gr.Markdown(
+                "Controls the randomness of predictions. Lower values make the model more deterministic."
+            )
+            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top P")
+            gr.Markdown(
+                "Nucleus sampling. Lower values focus on more likely predictions."
+            )
+            repetition_penalty = gr.Slider(
+                minimum=1.0, maximum=2.0, value=1.2, label="Repetition Penalty"
+            )
+            gr.Markdown(
+                "Penalizes repeated words. Higher values discourage repetition."
+            )
+            top_k = gr.Slider(minimum=0, maximum=100, value=50, label="Top K")
+            gr.Markdown("Keeps only the top k predictions. Set to 0 for no limit.")
+
+    question.submit(send_action)
+
+    send_button.click(
+        fn=answer_question,
+        inputs=[history, temperature, top_p, repetition_penalty, top_k, question],
+        outputs=[history, question],
+    )
+
+app.launch()

requirements.txt

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+torch
+gradio
+transformers