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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the model and tokenizer outside the function to avoid
# reloading it on every call.
model_name = "gustavokuklinski/aeon-360m"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
# Apply the chat template to format the messages
# The jinja template from the model card can be used
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
)
# Generate the response
outputs = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
)
# Decode the generated text and yield the response
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
yield response
# The rest of your Gradio ChatInterface code remains the same
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="Your name is Aeon. Answer the user's question concisely using **only** the provided CONTEXT. If the CONTEXT doesn't contain the answer, state: 'I don't know, can we search?'. Do not add any information not present in the CONTEXT. Your responses must be in plain, natural language. **Factual questions:** Use the provided CONTEXT to form a single, comprehensive answer. Do not use information outside the CONTEXT. If the CONTEXT does not contain the answer, state: 'I don't know about it. Can we search?'. **Conversational questions:** Respond naturally and conversationally, without using the CONTEXT. Never echo the user's question or the CONTEXT.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.5, 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__":
chatbot.launch() |