Add space

app.py

@@ -1,7 +1,42 @@
+# import gradio as gr
+
+# def greet(name):
+#     return "Hello " + name + "!!"
+
+# demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# demo.launch()
+
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load base model
+base_model = "microsoft/Phi-4-mini-instruct"
+lora_path = "./phi4-lora-finetuned-10k"  # or use HF repo name if pushed
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
+
+base = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+model = PeftModel.from_pretrained(base, lora_path)
+
+def generate(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    output = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+gr.Interface(fn=generate, inputs="text", outputs="text").launch()

requirements.txt

@@ -0,0 +1,6 @@
+transformers>=4.41.0
+peft>=0.10.0
+bitsandbytes
+accelerate
+gradio
+torch>=2.1