Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+# Replace with your model name
+#MODEL_NAME = "ssirikon/Gemma7b-bnb-Unsloth"
+MODEL_NAME = "unsloth/gemma-7b-bnb-4bit"
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME, 
+    device_map="auto", 
+    torch_dtype=torch.float16,
+    load_in_4bit=True,  # Load the model in 4-bit precision
+    # Removed the unsupported argument
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# **Change 1: Set `llm_int8_skip_modules` to avoid deep copy**
+#model.quantization_config.llm_int8_skip_modules = ['lm_head'] 
+
+# Create a pipeline for text generation
+generator = pipeline(
+    task="text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_new_tokens=50,  # Adjust as needed
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+def generate_text(email):
+    result = generator("Generate a subject line for the following email.\n"+email)
+    return result[0]["generated_text"]
+
+
+# Create a Gradio interface
+demo = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=5, label="Enter your Email here:"),
+    outputs=gr.Textbox(label="Generated Subject"),
+    title="Email Subject Generation demo",
+    description="Enter an email and let the model generate the subject for you!",
+)
+
+demo.launch()