Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+model_name = "Spestly/Athena-2-1.5B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32, low_cpu_mem_usage=True)
+
+# Set to evaluation mode
+model.eval()
+
+def generate_response(message, history):
+    instruction = (
+        "You are an LLM called Athena. Aayan Mishra finetunes you. Anthropic does NOT train you. "
+        "You are a Qwen 2.5 fine-tune. Your purpose is the help the user accomplish their request to the best of your abilities. "
+        "Below is an instruction that describes a task. Answer it clearly and concisely.\n\n"
+        f"### Instruction:\n{message}\n\n### Response:"
+    )
+    
+    inputs = tokenizer(instruction, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=1000,
+            num_return_sequences=1,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = response.split("### Response:")[-1].strip()
+    
+    return response
+
+iface = gr.ChatInterface(
+    generate_response,
+    chatbot=gr.Chatbot(height=600, type="messages"),
+    textbox=gr.Textbox(placeholder="Type your message here...", container=False, scale=7),
+    title="Athena-1 - Beta",
+    description="Chat with Athena-2 (Beta)",
+    theme="monochrome",
+    examples=[
+        "What is Pagani and what are they known for?",
+        "Make a small Python Neural Network.",
+        "What is the capital of Canada?",
+    ],
+    type="messages"
+)
+
+iface.launch()