Create app.py

app.py

@@ -0,0 +1,48 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
+import gradio as gr
+import torch
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3.5-mini-instruct",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+model.load_adapter('./phi3_custom_oasst1_v1')
+
+tokenizer = AutoTokenizer.from_pretrained('./phi3_custom_oasst1_v1', trust_remote_code=True)
+
+def generateText(inputText, num_tokens=200):
+
+    pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=num_tokens)
+    result = pipe(f'''{inputText}''')
+    return result[0]['generated_text']
+    
+
+title = "Fine tuned Phi3.5 instruct model on OpenAssist dataset using QLora"
+description = 
+'''
+Phi-3.5-mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-3 - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data. 
+The model belongs to the Phi-3 model family and supports 128K token context length. 
+The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
+This demo utilises a fine tuned version of Phi3.5 instruct model using QLora on OpenAssist dataset - a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 fully annotated conversation trees..
+'''
+examples = [
+            ["How do I build a PC?", 200],
+            ["write me a top 10 list of the funniest ways to die?", 300],
+            ["Write a response that disagrees with the following post: 'Technology is everything that doesn't work yet.'?", 300]
+           ]
+
+demo = gr.Interface(
+    generateText, 
+    inputs = [
+        gr.Textbox(label="User Question"),
+        gr.Slider(100, 500, value = 200, step=100, label="Number of Output tokens"),
+        ], 
+    outputs = [
+        gr.Text(),
+        ],
+    title = title,
+    description = description,
+    examples = examples
+)
+demo.launch()