Spaces:
Sleeping
Sleeping
File size: 3,526 Bytes
a1918ea db6559c a1918ea |
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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
import gradio as gr
import torch
from transformers import pipeline
model_name = "eljanmahammadli/AzLlama-152M-Alpaca"
model = pipeline("text-generation", model=model_name, torch_dtype=torch.float16)
logo_path = "AzLlama-logo.webp"
def get_prompt(question):
base_instruction = "Aşağıda tapşırığı təsvir edən təlimat və əlavə kontekst təmin edən giriş verilmiştir. Sorğunu uyğun şəkildə tamamlayan cavab yazın."
prompt = f"""{base_instruction}
### Təlimat:
{question}
### Cavab:
"""
return prompt
def get_answer(llm_output):
return llm_output.split("### Cavab:")[1].strip()
def answer_question(history, temperature, top_p, repetition_penalty, top_k, question):
model_params = {
"temperature": temperature,
"top_p": top_p,
"repetition_penalty": repetition_penalty,
"top_k": top_k,
"max_length": 512, # Adjust based on your needs
"do_sample": True,
}
prompt = get_prompt(question)
llm_output = model(prompt, **model_params)[0]
answer = get_answer(llm_output["generated_text"])
divider = "\n\n" if history else ""
print(answer)
new_history = history + divider + f"USER: {question}\nASSISTANT: {answer}\n"
return new_history, "" # Return updated history and clear the question input
def send_action(_=None):
send_button.click()
with gr.Blocks() as app:
gr.Markdown("# AzLlama-150M Chatbot\n\n")
with gr.Row():
with gr.Column(scale=0.2, min_width=200):
gr.Markdown("### Model Logo")
gr.Image(
value=logo_path,
)
# write info about the model
gr.Markdown(
"### Model Info\n"
"This model is a 150M paramater LLaMA2 model trained from scratch on Azerbaijani text. It can be used to generate text based on the given prompt. "
)
with gr.Column(scale=0.6):
gr.Markdown("### Chat with the Assistant")
history = gr.Textbox(
label="Chat History", value="", lines=20, interactive=False
)
question = gr.Textbox(
label="Your question",
placeholder="Type your question and press enter",
)
send_button = gr.Button("Send")
with gr.Column(scale=0.2, min_width=200):
gr.Markdown("### Model Settings")
temperature = gr.Slider(
minimum=0.1, maximum=1.0, value=0.9, label="Temperature"
)
gr.Markdown(
"Controls the randomness of predictions. Lower values make the model more deterministic."
)
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top P")
gr.Markdown(
"Nucleus sampling. Lower values focus on more likely predictions."
)
repetition_penalty = gr.Slider(
minimum=1.0, maximum=2.0, value=1.2, label="Repetition Penalty"
)
gr.Markdown(
"Penalizes repeated words. Higher values discourage repetition."
)
top_k = gr.Slider(minimum=0, maximum=100, value=50, label="Top K")
gr.Markdown("Keeps only the top k predictions. Set to 0 for no limit.")
question.submit(send_action)
send_button.click(
fn=answer_question,
inputs=[history, temperature, top_p, repetition_penalty, top_k, question],
outputs=[history, question],
)
app.launch()
|