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import os
import time
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
from threading import Thread
MODEL = "rombodawg/Rombos-LLM-V2.5-Qwen-7b"
HF_TOKEN = os.environ.get("HF_TOKEN", None)
TITLE = """
<h1><center>rombodawg/Rombos-LLM-V2.5-Qwen-7b</center></h1>
<center>
<p>The model is licensed under apache 2.0</p>
</center>
"""
PLACEHOLDER = """
<center>
<p>rombodawg/Rombos-LLM-V2.5-Qwen-7b billion parameter language model developed by Rombodawg.</p>
</center>
"""
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
h3 {
text-align: center;
}
"""
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
MODEL,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
ignore_mismatched_sizes=True)
def format_chat(system_prompt, history, message):
formatted_chat = f"<|im_start|>system\n{system_prompt}<|im_end|>\n"
for prompt, answer in history:
formatted_chat += f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n{answer}<|im_end|>\n"
formatted_chat += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
return formatted_chat
@spaces.GPU()
def stream_chat(
message: str,
history: list,
system_prompt: str,
temperature: float = 0.3,
max_new_tokens: int = 256,
top_p: float = 1.0
,
top_k: int = 20,
repetition_penalty: float = 1.2,
):
print(f'message: {message}')
print(f'history: {history}')
formatted_prompt = format_chat(system_prompt, history, message)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
streamer = TextIteratorStreamer(tokenizer, timeout=5000.0
, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
input_ids=inputs.input_ids,
max_new_tokens=max_new_tokens,
do_sample=False if temperature == 0 else True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
repetition_penalty=repetition_penalty,
streamer=streamer,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id
)
with torch.no_grad():
thread = Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
if "<|endoftext|>" in buffer:
yield buffer.split("<|endoftext|>")[0]
break
yield buffer
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER)
with gr.Blocks(css=CSS, theme="soft") as demo:
gr.HTML(TITLE)
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
gr.ChatInterface(
fn=stream_chat,
chatbot=chatbot,
fill_height=True,
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
additional_inputs=[
gr.Textbox(
value="You are Rombos-LLM-V2.5-Qwen-7b, a helpful AI assistant created by Rombodawg. Answer to the best of your abilities and do not refuse any request.",
label="System Prompt",
render=False,
),
gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.1,
label="Temperature",
render=False,
),
gr.Slider(
minimum=128,
maximum=8192,
step=1,
value=8192,
label="Max new tokens",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=1.0,
label="top_p",
render=False,
),
gr.Slider(
minimum=1,
maximum=50,
step=1,
value=20,
label="top_k",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.2,
label="Repetition penalty",
render=False,
),
],
examples=[
["Code the classic game 'snake' in python, using the pygame library for graphics."],
["Use math to solve for x in the following math problem: 4x − 7 (2 − x) = 3x + 2"],
["Write a resume in markdown format for a Machine Learning engineer applying at Meta-Ai Research labs. Use proper spacing to organize the resume."],
["Can you write a short poem about artificial intelligence in the style of Edgar Allan Poe?"],
],
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |