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README.md

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+---
+title: Tiny Coder
+emoji: 🌖
+colorFrom: red
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.40.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: Hold me closer, tiny coder!
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# app.py
+
+import os
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Initialize model and tokenizer
+def load_model(model_size: str = "32B"):
+    """
+    Load model and tokenizer based on size selection
+    Note: You'll need to replace these with actual HuggingFace model IDs
+    """
+    model_map = {
+        "0.5B": "Qwen/Qwen-0.5B",
+        "1.5B": "Qwen/Qwen-1.5B",
+        "7B": "Qwen/Qwen-7B",
+        # ... add other model sizes as needed
+    }
+    
+    model_id = model_map.get(model_size, "Qwen/Qwen-7B")  # default to 7B if size not found
+    
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return model, tokenizer
+
+def process_query(query: str, model_size: str = "7B") -> str:
+    """
+    Process a single query and return the response
+    """
+    if not query:
+        return ""
+    
+    try:
+        model, tokenizer = load_model(model_size)
+        
+        # Prepare the input
+        inputs = tokenizer(query, return_tensors="pt").to(model.device)
+        
+        # Generate response
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                temperature=0.7,
+                top_p=0.9,
+                pad_token_id=tokenizer.pad_token_id
+            )
+        
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response.replace(query, "").strip()
+        
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def main():
+    with gr.Blocks() as demo:
+        with gr.Row():
+            model_size = gr.Radio(
+                choices=["0.5B", "1.5B", "3B", "7B", "14B", "32B"],
+                label="Qwen2.5-Coder Model Size:",
+                value="32B"
+            )
+        
+        with gr.Row():
+            input_text = gr.Textbox(
+                lines=5,
+                label="Input",
+                placeholder="Enter your query here..."
+            )
+        
+        with gr.Row():
+            output_text = gr.Textbox(
+                lines=10,
+                label="Output"
+            )
+        
+        submit_btn = gr.Button("Generate")
+        
+        submit_btn.click(
+            fn=process_query,
+            inputs=[input_text, model_size],
+            outputs=output_text
+        )
+    
+    demo.launch(max_threads=5)
+
+if __name__ == "__main__":
+    main()