File size: 1,969 Bytes
fe3674f |
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 |
import gradio as gr
from huggingface_hub import InferenceClient
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="BioMistral/BioMistral-7B", torch_dtype=torch.bfloat16, device_map="auto")
@spaces.GPU
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_k=50, top_p=top_p)
last_space_index = outputs[0]["generated_text"].rfind('[/INST]')
# Extract the substring after the last space character
substring_after_last_space = outputs[0]["generated_text"][last_space_index + 7:]
yield substring_after_last_space
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)",
),
gr.Textbox(label="Hugging Face Token", placeholder="Enter your Hugging Face token here"),
],
css="footer{display:none !important}",
)
if __name__ == "__main__":
demo.launch() |