Baptiste Pannier
update demo
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import json
import os
import shutil
import requests
import gradio as gr
from huggingface_hub import Repository, InferenceClient
HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/hf-extreme-scalcon-180B-chat "
BOT_NAME = "Falcon"
STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]
EXAMPLES = [
["Hey Falcon! 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, Falcon LLM?"]
]
client = InferenceClient(
API_URL,
headers={"Authorization": f"Bearer {HF_TOKEN}"},
)
def format_prompt(message, history, system_prompt):
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"Falcon: {bot_response}\n" # Response already contains "Falcon: "
prompt += f"""User: {message}
Falcon:"""
return prompt
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)
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=42,
)
formatted_prompt = format_prompt(prompt, history, system_prompt)
print(formatted_prompt)
stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
previous_token = ""
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
previous_token = response.token.text
yield output
return output
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=8192,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
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:
with gr.Row():
with gr.Column():
gr.Image("home-banner.jpg", elem_id="banner-image", show_label=False)
with gr.Column():
gr.Markdown(
"""**Chat with [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct), brainstorm ideas, discuss your holiday plans, and more!**
✨ This demo is powered by [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b), finetuned on the [Baize](https://github.com/project-baize/baize-chatbot) dataset. [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is a state-of-the-art large language model built by the [Technology Innovation Institute](https://www.tii.ae) in Abu Dhabi. It is trained on 1 trillion tokens (including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)) and available under the Apache 2.0 license. It currently holds the 🥇 1st place on the [🤗 Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
🧪 This is only a **first experimental preview**: we intend to provide increasingly capable versions of Falcon Chat in the future, based on improved datasets and RLHF/RLAIF.
👀 **Learn more about Falcon LLM:** [falconllm.tii.ae](https://falconllm.tii.ae/)
➡️️ **Intended Use**: this demo is intended to showcase an early finetuning of [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b), to illustrate the impact (and limitations) of finetuning on a dataset of conversations and instructions. We encourage the community to further build upon the base model, and to create even better instruct/chat versions!
⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.
"""
)
gr.ChatInterface(
generate,
examples=EXAMPLES,
additional_inputs=additional_inputs,
)
demo.queue(concurrency_count=16).launch(debug=True)