Upload app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model = AutoModelForCausalLM.from_pretrained("checkpoint",trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("checkpoint", trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+def inference(prompt, count):
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) 
+    result = pipe(f"### Human: {prompt}",max_new_tokens=count)
+    out_text = result[0]['generated_text']
+    return out_text
+
+title = "TSAI S21 Assignment: Adaptive QLoRA training on open assist oasst1 dataset, using microsoft/phi2 model"
+description = "A simple Gradio interface that accepts a context and generates GPT like text "
+examples = [["What is a large language model?","200"],
+            ["Explain about monopsony","200"]
+           ]
+ 
+
+demo = gr.Interface(
+    inference, 
+    inputs = [gr.Textbox(placeholder="Enter a prompt"), gr.Textbox(placeholder="Enter number of characters you want to generate")], 
+    outputs = [gr.Textbox(label="Chat GPT like text")],
+    title = title,
+    description = description,
+    examples = examples
+)
+demo.launch()