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import gradio as gr
from hqq.engine.hf import HQQModelForCausalLM, AutoTokenizer
import torch, transformers
from threading import Thread
#Load the model
model_id = 'mobiuslabsgmbh/Llama-2-7b-chat-hf_1bitgs8_hqq'
model = HQQModelForCausalLM.from_quantized(model_id, adapter='adapter_v0.1.lora')
tokenizer = AutoTokenizer.from_pretrained(model_id)
#Setup Inference Mode
tokenizer.add_bos_token = False
tokenizer.add_eos_token = False
if not tokenizer.pad_token: tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.config.use_cache = True
model.eval();
# Optional: torch compile for faster inference
model = torch.compile(model)
def chat_processor(chat, max_new_tokens=100, do_sample=True, device='cpu'):
tokenizer.use_default_system_prompt = False
streamer = transformers.TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_params = dict(
tokenizer("<s> [INST] " + chat + " [/INST] ", return_tensors="pt").to(device),
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=do_sample,
pad_token_id=tokenizer.pad_token_id,
top_p=0.90 if do_sample else None,
top_k=50 if do_sample else None,
temperature= 0.6 if do_sample else None,
num_beams=1,
repetition_penalty=1.2,
)
t = Thread(target=model.generate, kwargs=generate_params)
t.start()
#print("User: ", chat);
#print("Assistant: ");
#outputs = ""
#for text in streamer:
# outputs += text
# print(text, end="", flush=True)
#torch.cuda.empty_cache()
return streamer
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
def user(user_message, history):
return "", history + [[user_message, None]]
def bot(history):
print("Question: ", history[-1][0])
stream = chat_processor(chat=history[-1][0])
history[-1][1] = ""
for character in stream:
print(character)
history[-1][1] += character
yield history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(bot, chatbot, chatbot)
clear.click(lambda: None, None, chatbot, queue=False)
demo.queue()
demo.launch() |