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import os
import gradio as gr
import mdtex2html
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, MistralConfig
# Initialize model and tokenizer
model_name_or_path = "teknium/OpenHermes-2-Mistral-7B"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
load_in_8bit=True,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
config = MistralConfig()
# Text parsing function
def _parse_text(text):
lines = text.split("\n")
lines = [line for line in lines if line != ""]
count = 0
for i, line in enumerate(lines):
if "```" in line:
count += 1
items = line.split("`")
if count % 2 == 1:
lines[i] = f'<pre><code class="language-{items[-1]}">'
else:
lines[i] = f"<br></code></pre>"
else:
if i > 0:
if count % 2 == 1:
line = line.replace("`", r"\`")
line = line.replace("<", "<")
line = line.replace(">", ">")
line = line.replace(" ", " ")
line = line.replace("*", "*")
line = line.replace("_", "_")
line = line.replace("-", "-")
line = line.replace(".", ".")
line = line.replace("!", "!")
line = line.replace("(", "(")
line = line.replace(")", ")")
line = line.replace("$", "$")
lines[i] = "<br>" + line
text = "".join(lines)
return text
# Demo launching function
def _launch_demo(args, model, tokenizer, config):
def predict(_query, _chatbot, _task_history):
print(f"User: {_parse_text(_query)}")
_chatbot.append((_parse_text(_query), ""))
# Tokenize the input
input_ids = tokenizer.encode(_query, return_tensors='pt')
print("Input IDs:", input_ids)
# Move input_ids to CUDA if available
input_ids = input_ids.to('cuda')
# Generate attention_mask
attention_mask = torch.ones(input_ids.shape).to('cuda')
# Generate a response using the model
generated_ids = model.generate(input_ids, max_length=300)
print("Generated IDs:", generated_ids)
# Decode the generated IDs to text
full_response = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
# Update the chatbot state
_chatbot[-1] = (_parse_text(_query), _parse_text(full_response))
yield _chatbot
print(f"History: {_task_history}")
_task_history.append((_query, full_response))
print(f"OpenHermes: {_parse_text(full_response)}")
def regenerate(_chatbot, _task_history):
if not _task_history:
yield _chatbot
return
item = _task_history.pop(-1)
_chatbot.pop(-1)
yield from predict(item[0], _chatbot, _task_history)
def reset_user_input():
return gr.update(value="")
def reset_state(_chatbot, _task_history):
_task_history.clear()
_chatbot.clear()
import gc
gc.collect()
torch.cuda.empty_cache()
return _chatbot
with gr.Blocks() as demo:
gr.Markdown("""
## OpenHermes V2 - Mistral 7B: Mistral 7B Based by Teknium!
**Space created by [@artificialguybr](https://twitter.com/artificialguybr). Model by [@Teknium1](https://twitter.com/Teknium1).Thanks HF for GPU!**
**OpenHermes V2 Mistral 7B was trained on 900,000 instructions, and surpasses all previous versions of Hermes 13B and below, and matches 70B on some benchmarks!**
""")
chatbot = gr.Chatbot(label='OpenHermes-V2', elem_classes="control-height", queue=True)
query = gr.Textbox(lines=2, label='Input')
task_history = gr.State([])
with gr.Row():
submit_btn = gr.Button("🚀 Submit")
empty_btn = gr.Button("🧹 Clear History")
regen_btn = gr.Button("🤔️ Regenerate")
submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True, queue=True) # Enable queue
submit_btn.click(reset_user_input, [], [query], queue=False) #No queue for resetting
empty_btn.click(reset_state, [chatbot, task_history], outputs=[chatbot], show_progress=True, queue=False) #No queue for clearing
regen_btn.click(regenerate, [chatbot, task_history], [chatbot], show_progress=True, queue=True) # Enable queue
demo.queue(max_size=20)
demo.launch()
# Main execution
if __name__ == "__main__":
_launch_demo(None, model, tokenizer, config) |