Initial space setup without model weights

README.md

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+---
+title: SmolLM2 Demo
+emoji: 🤖
+colorFrom: blue
+colorTo: red
+sdk: gradio
+sdk_version: 4.12.0
+app_file: app.py
+pinned: false
+---
+
+# SmolLM2 Demo
+
+This is a demo of the SmolLM2 language model, a small transformer-based model trained on custom text data.
+
+## Features
+- Text generation with adjustable parameters
+- Temperature control for creativity
+- Configurable output length
+
+## Usage
+1. Enter your prompt text
+2. Adjust the maximum length and temperature
+3. Click generate to see the model's output 

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer from HuggingFace
+model_name = "HuggingFaceTB/SmolLM2-135M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate(prompt, max_length=50, temperature=0.7):
+    """Generate text from prompt"""
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    # Generate text
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=max_length,
+        temperature=temperature,
+        do_sample=True,
+        top_p=0.9,
+        repetition_penalty=1.1
+    )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=generate,
+    inputs=[
+        gr.Textbox(label="Enter your prompt", value="Once upon a time"),
+        gr.Slider(minimum=10, maximum=200, value=50, label="Maximum length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="SmolLM2 Text Generation",
+    description="A small language model based on SmolLM2-135M architecture."
+)
+
+if __name__ == "__main__":
+    demo.launch() 

requirements.txt

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+torch
+transformers
+gradio