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import gradio as gr
from transformers import AutoModelForCausalLM
import torch
# Load the model
model_name = "wop/kosmox-gguf"
model = AutoModelForCausalLM.from_pretrained(model_name)
# Define the chat template function
def format_chat(messages, add_generation_prompt):
formatted = "<BOS>"
for message in messages:
if message['from'] == 'human':
formatted += ' ' + message['value'] + ' '
elif message['from'] == 'gpt':
formatted += ' ' + message['value'] + ' '
else:
formatted += '<|' + message['from'] + '|> ' + message['value'] + ' '
if add_generation_prompt:
formatted += ' '
return formatted
# Function to generate responses
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Prepare the chat history
messages = [{"from": "system", "value": system_message}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"from": "human", "value": user_msg})
if bot_msg:
messages.append({"from": "gpt", "value": bot_msg})
messages.append({"from": "human", "value": message})
# Format the chat input for the model
chat_input = format_chat(messages, add_generation_prompt=False)
# Tokenize input (assuming model can handle raw text inputs internally)
inputs = torch.tensor([ord(c) for c in chat_input]).unsqueeze(0) # Dummy tokenization
# Generate response
with torch.no_grad():
outputs = model.generate(
input_ids=inputs,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True
)
response = ''.join([chr(t) for t in outputs[0].tolist() if t < 256]) # Dummy decoding
yield response.strip()
# Define the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", 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)"),
],
)
# Launch the demo
if __name__ == "__main__":
demo.launch()
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