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import torch
from transformers import pipeline, logging, AutoModelForCausalLM, AutoTokenizer
import gradio as gr
## 1 - Loading Model
model_name = "microsoft/phi-2"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map='auto',
)
model.config.use_cache = False
## 2 - Loading Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
## 3 - Load adapter (trained LORA weights)
peft_model_folder = 'checkpoint700'
model.load_adapter(peft_model_folder)
def generate_dialogue(input_text):
pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer,max_length=200)
result = pipe(f"<s>[INST] {input_text} [/INST]")
return result[0]['generated_text']
HTML_TEMPLATE = """
<style>
#app-header {
text-align: center;
background: rgba(255, 255, 255, 0.3); /* Semi-transparent white */
padding: 20px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
position: relative; /* To position the artifacts */
}
#app-header h1 {
color: #FF0000;
font-size: 2em;
margin-bottom: 10px;
}
.concept {
position: relative;
transition: transform 0.3s;
}
.concept:hover {
transform: scale(1.1);
}
.concept img {
width: 100px;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
}
.concept-description {
position: absolute;
bottom: -30px;
left: 50%;
transform: translateX(-50%);
background-color: #4CAF50;
color: white;
padding: 5px 10px;
border-radius: 5px;
opacity: 0;
transition: opacity 0.3s;
}
.concept:hover .concept-description {
opacity: 1;
}
/* Artifacts */
</style>
<div id="app-header">
<!-- Artifacts -->
<div class="artifact large"></div>
<div class="artifact large"></div>
<div class="artifact large"></div>
<div class="artifact large"></div>
<!-- Content -->
<h1>CHAT with fine tuned Phi-2 LLM</h1>
<p>Generate dialogue for given some initial prompt for context.</p>
<p>Model: Phi-2 (https://huggingface.co/microsoft/phi-2), Dataset: oasst1 (https://huggingface.co/datasets/OpenAssistant/oasst1) </p>
"""
with gr.Blocks(theme=gr.themes.Glass(),css=".gradio-container {background: url('file=https://github.com/santule/ERA/assets/20509836/e78f2bb3-ddd8-4ce9-a941-3d3d7ef7a272')}") as interface:
gr.HTML(value=HTML_TEMPLATE, show_label=False)
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
gr.Markdown("")
with gr.Row():
input_text = gr.Textbox(
label="Input Text",
value="Enter your prompt here: This text will set the context for the AI's response."
)
outputs = gr.Textbox(
label="Answer"
)
inputs = [input_text]
with gr.Column():
button = gr.Button("Ask me")
button.click(generate_dialogue, inputs=inputs, outputs=outputs)
interface.launch() |