Create app.py

app.py

@@ -0,0 +1,20 @@
+from accelerate import Accelerator
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import os, random, gc
+import torch
+accelerator=Accelerator(cpu=True)
+mdl = "bigcode/starcoder2-3b"
+tokenizer = AutoTokenizer.from_pretrained(mdl)
+model = accelerator.prepare(AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16))
+def plex(ynputs):
+    onputs = tokenizer.encode(""+ynputs+"", return_tensors="pt").to("cpu")
+    iutputs = model.generate(onputs, max_new_tokens=350)
+    return tokenizer.decode(iutputs[0])
+with gr.Blocks() as iface:
+    zutput=gr.Textbox(lines=5)
+    znput=gr.Textbox(lines=2)
+    btn=gr.Button("ASK")
+    btn.click(fn=plex, inputs=znput, outputs=zutput)
+iface.queue(max_size=1,api_open=False)
+iface.launch(max_threads=20,inline=False,show_api=False)