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a42856d e716094 a42856d 3becfbe a42856d e716094 a42856d e716094 3becfbe e716094 a42856d e716094 a42856d e716094 a42856d e716094 a42856d e716094 a42856d e716094 b8ec93c a42856d e716094 a42856d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | from threading import Thread
import gradio as gr
import torch
from transformers import (AutoModelForCausalLM, AutoTokenizer,
TextIteratorStreamer)
MODEL_ID = "alibayram/gemma3-tr-v64k-it"
# Model ve tokenizer yükleme
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
device_map="auto",
)
def build_prompt(gecmis, kullanici_mesaji):
mesajlar = []
mesajlar.extend(gecmis)
mesajlar.append({
"role": "user",
"content": kullanici_mesaji
})
return tokenizer.apply_chat_template(
mesajlar,
tokenize=False,
add_generation_prompt=True,
)
def respond(
mesaj,
gecmis: list[dict[str, str]],
max_tokens,
temperature,
top_p,
):
prompt = build_prompt(gecmis, mesaj)
girisler = tokenizer(prompt, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(
tokenizer,
skip_prompt=True,
skip_special_tokens=True,
)
uretim_parametreleri = dict(
**girisler,
streamer=streamer,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
thread = Thread(target=model.generate, kwargs=uretim_parametreleri)
thread.start()
cevap = ""
for token in streamer:
cevap += token
yield cevap
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Slider(1, 1024, value=64, step=1, label="Maksimum Yeni Token"),
gr.Slider(0.1, 1.99, value=0.7, step=0.1, label="Sıcaklık (Temperature)"),
gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p"),
],
)
with gr.Blocks() as demo:
chatbot.render()
if __name__ == "__main__":
demo.launch() |