serpdotai/

llama-hh-lora-30B

import torch
import os
import transformers
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM

model_path = "decapoda-research/llama-30b-hf"
peft_path = 'serpdotai/llama-hh-lora-30B'
tokenizer_path = 'decapoda-research/llama-30b-hf'

model = LlamaForCausalLM.from_pretrained(model_path, load_in_8bit=True, device_map="auto") # or something like {"": 0}
model = PeftModel.from_pretrained(model, peft_path, torch_dtype=torch.float16, device_map="auto") # or something like {"": 0}
tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path)

batch = tokenizer("\n\nUser: Are you sentient?\n\nAssistant:", return_tensors="pt")

with torch.no_grad():
    out = model.generate(
        input_ids=batch["input_ids"].cuda(),
        attention_mask=batch["attention_mask"].cuda(),
        max_length=100,
        do_sample=True,
        top_k=50,
        top_p=1.0,
        temperature=1.0,
        use_cache=False
    )
print(tokenizer.decode(out[0]))