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Update app.py
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import os
import gradio as gr
from huggingface_hub import InferenceClient
HF_TOKEN = os.environ.get("HF_TOKEN", None)
model2api = [
"tiiuae/falcon-180B-chat",
"meta-llama/Llama-2-70b-chat-hf",
"codellama/CodeLlama-34b-Instruct-hf",
"victor/CodeLlama-34b-Instruct-hf",
"timdettmers/guanaco-33b-merged",
]
STOP_SEQUENCES = ["User:", "###", "<|endoftext|>", "</s>"]
EXAMPLES = [
["Hey LLAMA! Any recommendations for my holidays in Abu Dhabi?"],
["What's the Everett interpretation of quantum mechanics?"],
["Give me a list of the top 10 dive sites you would recommend around the world."],
["Can you tell me more about deep-water soloing?"],
["Can you write a short tweet about the release of our latest AI model, LLAMA LLM?"]
]
def format_prompt(message, history, system_prompt, bot_name):
prompt = ""
if system_prompt:
prompt += f"System: {system_prompt}\n"
for user_prompt, bot_response in history:
prompt += f"User: {user_prompt}\n"
prompt += f"{bot_name}: {bot_response}\n"
prompt += f"""User: {message}\n{bot_name}: """
return prompt
seed = 42
def generate(
prompt, history, system_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
temperature = float(temperature)
if temperature < 1e-2:
temperature = 1e-2
top_p = float(top_p)
global seed
generate_kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
stop_sequences=STOP_SEQUENCES,
do_sample=True,
seed=seed,
)
seed = seed + 1
client = InferenceClient()
clientList = (client.list_deployed_models('text-generation-inference'))['text-generation']
for model in model2api:
if model in clientList:
client = InferenceClient(model, token=HF_TOKEN)
print(f"Choosen model: {model}")
break
if model == model2api[0]:
bot_name = "Falcon"
else:
bot_name = "Assistant"
formatted_prompt = format_prompt(prompt, history, system_prompt, bot_name)
output = ""
try:
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
for response in stream:
output += response.token.text
for stop_str in STOP_SEQUENCES:
if output.endswith(stop_str):
output = output[:-len(stop_str)]
# output = output.rstrip()
yield output
yield output
return output
except Exception as e:
raise gr.Error(f"Error: {e}. Please retry!")
additional_inputs=[
gr.Textbox("", label="Optional system prompt"),
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=3000,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.01,
maximum=0.99,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
with gr.Blocks() as demo:
gr.ChatInterface(
generate,
examples=EXAMPLES,
additional_inputs=additional_inputs,
)
demo.queue(api_open=False).launch(show_api=False)
#demo.queue(concurrency_count=100).launch()