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app.py

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+import gradio as gr
+import spaces
+import json
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile"
+
+tokenizer = None
+model = None
+
+def load_model():
+    global tokenizer, model
+    if tokenizer is None:
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_ID,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        )
+    return tokenizer, model
+
+@spaces.GPU
+def agent_respond(task: str, history: list) -> str:
+    """A mobile-optimized agent that can answer questions, write code, and solve tasks.
+    
+    Powered by dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile — a 1B parameter model 
+    quantized to Q4, designed to run on phones. This proves real agents can run 
+    on pocket-sized models.
+    """
+    tokenizer, model = load_model()
+    
+    messages = [{"role": "system", "content": "You are a helpful mobile AI assistant. You are running on a 1B parameter model optimized for phones. Be concise and helpful."}]
+    for h in history:
+        messages.append({"role": "user", "content": h[0]})
+        if h[1]:
+            messages.append({"role": "assistant", "content": h[1]})
+    messages.append({"role": "user", "content": task})
+    
+    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=256,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
+    return response
+
+@spaces.GPU  
+def agent_code(instruction: str) -> str:
+    """Generate code using a mobile-optimized model."""
+    tokenizer, model = load_model()
+    
+    prompt = f"Write Python code for: {instruction}\n\n```python\n"
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.3,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+    
+    code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract code block
+    if "```python" in code:
+        code = code.split("```python")[1].split("```")[0]
+    return code.strip()
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue"), title="dispatchAI Mobile Agent") as demo:
+    gr.Markdown("""
+    # 🤖 dispatchAI Mobile Agent
+    
+    **A real AI agent running on a 1B parameter model — small enough for your pocket.**
+    
+    Model: [Llama-3.2-1B-Instruct-Q4-mobile](https://huggingface.co/dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile)
+    
+    This agent runs on a model quantized to Q4 (700MB file size), designed to run on 
+    Snapdragon 865 phones. It can answer questions, write code, and solve tasks — 
+    all on a model 1/100th the size of GPT-4.
+    
+    ## Try It
+    
+    - **Chat**: Ask the agent anything
+    - **Code**: Ask it to write Python code
+    
+    ## The Point
+    
+    This isn't about matching GPT-4. It's about proving that a 1B model on a phone 
+    can be genuinely useful. For the tasks people actually do on phones — quick answers, 
+    code snippets, summaries, classifications — a 1B model is enough.
+    """)
+    
+    with gr.Tab("💬 Chat"):
+        chat = gr.ChatInterface(
+            fn=agent_respond,
+            title="Chat with a 1B Mobile Agent",
+            description="Powered by Llama-3.2-1B-Instruct-Q4-mobile (700MB)",
+        )
+    
+    with gr.Tab("👨‍💻 Code"):
+        code_input = gr.Textbox(label="What should I code?", placeholder="A function that reverses a string")
+        code_btn = gr.Button("Generate Code", variant="primary")
+        code_output = gr.Code(label="Generated Code", language="python")
+        code_btn.click(fn=agent_code, inputs=code_input, outputs=code_output)
+    
+    with gr.Tab("ℹ️ About"):
+        gr.Markdown("""
+        ## How This Works
+        
+        This Space runs a **1 billion parameter Llama-3.2 model** quantized to 4-bit.
+        
+        | Metric | Value |
+        |--------|-------|
+        | Model | Llama-3.2-1B-Instruct |
+        | Params | 1B |
+        | Quantization | Q4 (4-bit) |
+        | File size | 700MB |
+        | RAM needed | ~1.1GB |
+        | Speed on Snapdragon 865 | ~18 tokens/sec |
+        | Speed on this Space (ZeroGPU) | Faster |
+        
+        ## Run This On Your Phone
+        
+        ```bash
+        # Download the GGUF
+        hf download dispatchAI/Llama-3.2-1B-Instruct-Q4-mobile model.gguf
+        
+        # Run with llama.cpp
+        llama-cli -m model.gguf -p "Hello!" -n 100 -t 4
+        ```
+        
+        ## The Thesis
+        
+        > A 1B model on a phone is not a compromise. It's a victory.
+        
+        6.8 billion smartphones. Most can't run a cloud LLM. But they CAN run a 1B model 
+        at 18 tokens/sec. That's fast enough for real-time chat, code completion, 
+        summarization, and classification.
+        
+        ---
+        🚀 [dispatchAI](https://huggingface.co/dispatchAI) — Small. Mobile. Free. UAE-built.
+        """)
+
+demo.launch(mcp_server=True)