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import gradio as gr
import torch
from transformers import AutoTokenizer
from open_lm.utils.transformers.hf_config import OpenLMConfig
from open_lm.utils.transformers.hf_model import OpenLMforCausalLM
title = """# 🙋🏻♂️ Welcome to Tonic's DCLM 1B"""
# Load the model and tokenizer
model_name = "TRI-ML/DCLM-1B-IT"
# Load the configuration, tokenizer, and model separately
config = OpenLMConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = OpenLMforCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="cuda", config=config)
# Define the prompt format
def create_prompt(instruction):
PROMPT = '''Below is an instruction that describes a task.\n\nWrite a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:'''
return PROMPT.format(instruction=instruction)
# Define the respond function for Gradio
def respond(message, history, system_message, max_tokens, temperature, top_p):
# Create the prompt
prompt = create_prompt(message)
# Tokenize the input
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch.device('cuda'))
# Generate the response
output = model.generate(input_ids, max_length=max_tokens, top_p=top_p, do_sample=True, temperature=temperature)
# Decode the response
response = tokenizer.decode(output[0][len(input_ids[0]):])
response = response.split("<|endoftext|>")[0]
return response
# Create Gradio ChatInterface
demo = gr.ChatInterface(
gr.markdown(title),
# gr.markdown(description),
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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