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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import gradio as gr
model_id = "linjc16/Panacea-7B-Chat"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
streamer = TextStreamer(tokenizer)
# Chat function
def chat(message, history=[]):
prompt = message
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
with torch.no_grad():
output = model.generate(
input_ids,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
reply = tokenizer.decode(output[0], skip_special_tokens=True)
return reply
# Gradio Interface
iface = gr.Interface(
fn=chat,
inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
outputs="text",
title="Panacea-7B-Chat"
)
iface.launch()
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