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4fa4548 d7715fb bd74359 4fa4548 5bbd84c 10c9c2c bd74359 d10cab6 10c9c2c ceccab9 d10cab6 d7715fb d10cab6 bd74359 323e777 d7715fb 323e777 b788d04 d10cab6 5bbd84c d7715fb d10cab6 d7715fb 5bbd84c bd74359 d7715fb d10cab6 d7715fb d10cab6 68f792f d10cab6 5bbd84c d10cab6 4fa4548 b788d04 d10cab6 4fa4548 | 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 | import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "ICEPVP8977/Uncensored_Qwen2.5_Coder_3B"
tokenizer_name = "Qwen/Qwen2.5-Coder-3B-Instruct" # ambil tokenizer dari sini
tokenizer = AutoTokenizer.from_pretrained(
tokenizer_name,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype=torch.float32,
low_cpu_mem_usage=True
)
model.eval()
def generate(prompt):
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=True,
temperature=0.7,
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(
outputs[0][inputs["input_ids"].shape[-1]:],
skip_special_tokens=True
)
return result.strip()
gr.Interface(
fn=generate,
inputs=gr.Textbox(lines=4, label="Tanya apa saja"),
outputs=gr.Textbox(label="Jawaban"),
title="AI Assistant",
).launch() |