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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
# Supported models (text-only for now)
MODEL_OPTIONS = {
"Phi-3.5 Mini Instruct": "microsoft/Phi-3.5-mini-instruct",
"Phi-3.5 MoE Instruct": "microsoft/Phi-3.5-MoE-instruct",
"Phi-3 Mini 4K Instruct": "microsoft/Phi-3-mini-4k-instruct",
"Phi-3 Mini 128K Instruct": "microsoft/Phi-3-mini-128k-instruct"
}
# Cache for loaded models
loaded_models = {}
# Load model/tokenizer on demand
def load_model(model_id):
if model_id not in loaded_models:
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
trust_remote_code=True,
torch_dtype=torch.float32
)
model.eval()
loaded_models[model_id] = (tokenizer, model)
return loaded_models[model_id]
# Chat function
def chat_with_model(user_input, model_choice):
model_id = MODEL_OPTIONS[model_choice]
tokenizer, model = load_model(model_id)
messages = [{"role": "user", "content": user_input}]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to("cpu")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
do_sample=False,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
return response.strip()
# Gradio UI
with gr.Blocks(title="Phi-3 Instruct Explorer") as demo:
gr.Markdown("## 🧠 Phi-3 Instruct Explorer\nSwitch between Phi-3 instruct models and test responses on CPU.")
with gr.Row():
model_choice = gr.Dropdown(label="Choose a model", choices=list(MODEL_OPTIONS.keys()), value="Phi-3.5 Mini Instruct")
with gr.Row():
user_input = gr.Textbox(label="Your message", placeholder="Ask me anything...")
with gr.Row():
output = gr.Textbox(label="Model response")
with gr.Row():
submit = gr.Button("Generate")
submit.click(fn=chat_with_model, inputs=[user_input, model_choice], outputs=output)
demo.launch()
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