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import gradio as gr
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM
# Load the fine-tuned model and tokenizer
model_name = "richardcsuwandi/llama2-javanese"
model = AutoPeftModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.bfloat16)
# Merge adapter with base
model = model.merge_and_unload()
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token_id = 0
tokenizer.padding_side = "left"
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Format the input text
input_text = f"<s>[INST] <<SYS>> {system_message} <</SYS>> {message} [/INST]"
# Tokenize the input text
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
# Generate response
output_sequences = model.generate(
input_ids=inputs['input_ids'],
max_length=max_tokens + inputs['input_ids'].shape[1], # Adjust for input length
repetition_penalty=1.2,
temperature=temperature,
top_p=top_p
)
# Decode the generated response
generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
return generated_text
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="Sampeyan minangka chatbot umum sing tansah mangsuli nganggo basa Jawa.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |