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import gradio as gr
from huggingface_hub import InferenceClient
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
Generate a response using the Dolphin 2.9.1 Llama 3 70B model
"""
client = InferenceClient(token=hf_token.token, model="dphn/dolphin-2.9.1-llama-3-70b")
# Format the messages according to the ChatML template that Dolphin expects
formatted_prompt = f"<|im_start|>system\n{system_message}<|im_end|>\n"
# Add history messages
for entry in history:
if entry["role"] == "user":
formatted_prompt += f"<|im_start|>user\n{entry['content']}<|im_end|>\n"
elif entry["role"] == "assistant":
formatted_prompt += f"<|im_start|>assistant\n{entry['content']}<|im_end|>\n"
# Add the current user message
formatted_prompt += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
response = ""
# Send the formatted prompt to the model
for token in client.text_generation(
formatted_prompt,
max_new_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are Dolphin, a helpful AI assistant.", 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)",
),
],
)
with gr.Blocks() as demo:
gr.Markdown("# Dolphin 2.9.1 Llama 3 70B Demo")
gr.Markdown("This is a demo of the Dolphin 2.9.1 Llama 3 70B model. Note that this model is uncensored.")
gr.Markdown("### Warning:")
gr.Markdown("This model is uncensored and may comply with any requests, including unethical ones. Use responsibly.")
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch() |