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Probably don't use this model, I'm just tinkering, but it's a multi-turn, multi-speaker model attempt trained from /r/wallstreetbets data that you can find: https://huggingface.co/datasets/Sentdex/WSB-003.005

#https://huggingface.co/docs/peft/quicktour 

from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch

model = AutoPeftModelForCausalLM.from_pretrained("Sentdex/Walls1337bot-Llama2-7B-003.005.5000")
tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")

model = model.to("cuda")
model.eval()

prompt = "Your text here."
formatted_prompt = f"### BEGIN CONVERSATION ###\n\n## Speaker_0: ##\n{prompt}\n\n## Walls1337bot: ##\n"
inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"), max_new_tokens=128)
print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])
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