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import gradio as gr
import transformers
import torch
from peft import PeftModel
model_id = "JerniganLab/interviews-and-qa"
pipeline = transformers.pipeline(
"text-generation",
model="meta-llama/Meta-Llama-3-8B-Instruct",
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
pipeline.model = PeftModel.from_pretrained(model=base_model, model_id)
def chat_function(message, history, system_prompt, max_new_tokens, temperature):
messages = [{"role":"system","content":system_prompt},
{"role":"user", "content":message}]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")]
outputs = pipeline(
prompt,
max_new_tokens = max_new_tokens,
eos_token_id = terminators,
do_sample = True,
temperature = temperature + 0.1,
top_p = 0.9,)
return outputs[0]["generated_text"][len(prompt):]
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
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()
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