File size: 1,625 Bytes
5fd0c28 2af305a 8a91905 2af305a 39fa555 2af305a f613acc 5fd0c28 2af305a 408d189 2af305a 5fd0c28 2af305a 5fd0c28 2af305a 5fd0c28 2af305a 5fd0c28 b2af35c 2af305a |
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 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load your model and tokenizer
model_name = "Mat17892/llama_lora_G14" # Replace with your Hugging Face model name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
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})
# Prepare input for the model
input_text = message
inputs = tokenizer(input_text, return_tensors="pt")
# Generate response
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Create the Gradio interface
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"),
],
)
if __name__ == "__main__":
demo.launch()
|