Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 1. Define your repository
+repo_name = "Phase-Technologies/qwen2.5-math-1.5b-generalized-merged"
+
+print("Loading model into memory... This takes a minute on a CPU.")
+
+# 2. Load the Tokenizer and Model
+# We load in standard precision because the free tier does not have a GPU for 4-bit
+tokenizer = AutoTokenizer.from_pretrained(repo_name)
+model = AutoModelForCausalLM.from_pretrained(
+    repo_name,
+    device_map="cpu",
+    torch_dtype=torch.float32 
+)
+
+# 3. Define the inference function
+def generate_response(prompt):
+    # Apply your training template
+    universal_prompt = "### Instruction:\n{}\n\n### Response:\n{}"
+    formatted_prompt = universal_prompt.format(prompt, "")
+    
+    # Tokenize input
+    inputs = tokenizer(
+        formatted_prompt, 
+        return_tensors="pt"
+    ).to(model.device)
+    
+    # Generate output
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=1024,
+            max_length=None,
+            use_cache=True,
+            repetition_penalty=1.15,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    # Decode and format the response
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    final_answer = response.split("### Response:\n")[-1]
+    
+    return final_answer
+
+# 4. Build the Gradio Web UI
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 Phase-Technologies: Generalized Qwen-Math (1.5B)")
+    gr.Markdown("An ultra-lightweight reasoning model fine-tuned for graduate-level proofs and conversational instruction-following.")
+    
+    with gr.Row():
+        with gr.Column():
+            user_input = gr.Textbox(
+                lines=5, 
+                label="Your Prompt", 
+                placeholder="E.g., What is 2+2? OR Provide a step-by-step proof for the eigenvalues of [[2,1],[1,2]]..."
+            )
+            submit_btn = gr.Button("Generate Response", variant="primary")
+            
+        with gr.Column():
+            output_box = gr.Textbox(lines=15, label="Model Output")
+            
+    submit_btn.click(fn=generate_response, inputs=user_input, outputs=output_box)
+
+# 5. Launch the app
+demo.launch()