File size: 2,244 Bytes
f2dbcad b470e0c ee66bc4 49583f4 ee66bc4 49583f4 ee66bc4 49583f4 61bb9bd 49583f4 ee66bc4 49583f4 ee66bc4 49583f4 ee66bc4 49583f4 f2dbcad 49583f4 f2dbcad |
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 |
import gradio as gr
import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Chronoboros-33B-GPTQ")
model = AutoModelForCausalLM.from_pretrained("TheBloke/Chronoboros-33B-GPTQ", device_map="auto")
model.eval() # set model to evaluation mode
# Optional: Use torch.compile() if you're on PyTorch 2.0+ for further speed-up
# model = torch.compile(model)
@spaces.GPU
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# Build the prompt using conversation history
prompt = f"{system_message}\n"
for user_text, assistant_text in history:
if user_text:
prompt += f"User: {user_text}\n"
if assistant_text:
prompt += f"Assistant: {assistant_text}\n"
prompt += f"User: {message}\nAssistant: "
# Tokenize the prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
# Generate the response with no gradients
with torch.no_grad():
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
)
# Extract the new tokens
new_tokens = output_ids[0][input_ids.shape[1]:]
# Stream output in chunks (e.g., 5 tokens per chunk)
chunk_size = 5
for i in range(0, new_tokens.shape[0], chunk_size):
current_response = tokenizer.decode(new_tokens[: i + chunk_size], skip_special_tokens=True)
yield current_response
# Configure the ChatInterface with additional inputs
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)",
),
],
)
if __name__ == "__main__":
demo.launch()
|