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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "lamapi/next-1b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
model.eval()
def chat(message, history):
history = history or []
prompt = "".join([f"User: {u}\nBot: {b}\n" for u,b in history])
prompt += f"User: {message}\nBot:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.inference_mode():
outputs = model.generate(
**inputs,
max_new_tokens=150,
do_sample=True,
temperature=0.8,
top_p=0.9
)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
reply = text.split("Bot:")[-1].strip()
history.append((message, reply))
return reply, history
iface = gr.ChatInterface(
fn=chat,
title="Next-1B Chatbot ⚡",
description="Lamapi model ile hızlı, kuyruksuz chat!",
).launch(share=True, server_name="0.0.0.0", server_port=7860, concurrency_count=4, queue=False)
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