Upload app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(".")
+
+# Load base model with quantization
+bnb_config = BitsAndBytesConfig(load_in_4bit=True)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/Meta-Llama-3.1-8B-bnb-4bit",  # same base you fine-tuned
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+# Load LoRA adapters
+model = PeftModel.from_pretrained(base_model, ".")
+
+# Create Gradio Interface
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(label="Enter your instruction"),
+    outputs=gr.Textbox(label="Model response"),
+    title="LLaMA 3 - Fine-tuned Model"
+).launch()