File size: 2,848 Bytes
ef831a4 99ae753 ef831a4 99ae753 ef831a4 f3902de ef831a4 99ae753 ef831a4 99ae753 ef831a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
from huggingface_hub import InferenceClient
import gradio as gr
inference_client = InferenceClient("google/gemma-7b-it")
# format prompt as per the chat template on the official model page: https://huggingface.co/google/gemma-7b-it
def format_prompt(input_text, history):
prompt = ""
if history:
for previous_prompt, response in history:
prompt += f"""<start_of_turn>user
{previous_prompt}<end_of_turn>
<start_of_turn>model
{response}<end_of_turn>"""
prompt += f"""<start_of_turn>user
{input_text}<end_of_turn>
<start_of_turn>model"""
return prompt
def generate(prompt, history, temperature=0.95, max_new_tokens=512, top_p=0.9, repetition_penalty=1.0):
if not history:
history = []
kwargs = dict(
temperature=temperature,
max_new_tokens=max_new_tokens,
top_p=top_p,
repetition_penalty=repetition_penalty,
do_sample=True,
)
formatted_prompt = format_prompt(prompt, history)
response = inference_client.text_generation(formatted_prompt, **kwargs, stream=True, details=True, return_full_text=False)
output = ""
for chunk in response:
output += chunk.token.text
yield output
return output
additional_inputs=[
gr.Slider(
label="Temperature",
value=0.85,
minimum=0.1,
maximum=1.0,
step=0.05,
interactive=True,
info="A higher value (> 1) will generate randomness and variability in the model response",
),
gr.Slider(
label="Max new tokens",
value=512,
minimum=128,
maximum=1048,
step=64,
interactive=True,
info="The maximum numbers of new tokens generated in the model response",
),
gr.Slider(
label="Top-p (random sampling)",
value=0.80,
minimum=0.1,
maximum=1,
step=0.05,
interactive=True,
info="A smaller value generates the highest probability tokens, a higher value (~ 1) allows low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.0,
minimum=0.5,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalizes repeated tokens in model response",
)
]
chatbot = gr.Chatbot(height=500)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.HTML("<center><h1>Google Gemma 7B IT</h1><center>")
gr.ChatInterface(
generate,
chatbot=chatbot,
retry_btn=None,
undo_btn=None,
clear_btn="Clear",
description="This chatbot is using a Hugging Face Inference Client for the google/gemma-7b-it model.",
additional_inputs=additional_inputs,
examples=[["Explain artificial intelligence in a few lines."]]
)
demo.queue().launch() |