metadata
library_name: peft
Finetuned dataset
- NTU NLP Lab's translated alapaca-tw_en dataset: alpaca-tw_en-align.json: ntunpllab translate Stanford Alpaca 52k dataset
Use which pretrained model
- NousResearch: https://huggingface.co/NousResearch/Llama-2-7b-chat-hf
Training procedure
The following bitsandbytes quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.4.0
Usage
Installation dependencies
$pip install transformers torch peft
Run the inference
import transformers
import torch
from transformers import AutoTokenizer, TextStreamer
from peft import AutoPeftModelForCausalLM
# Use the same tokenizer from the source model
original_model_path="NousResearch/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(original_model_path, use_fast=False)
# Load qlora fine-tuned model, you can replace this with your own model
qlora_model_path = "weiren119/traditional_chinese_qlora_llama2"
model = AutoPeftModelForCausalLM.from_pretrained(
qlora_model_path,
load_in_4bit=qlora_model_path.endswith("4bit"),
torch_dtype=torch.float16,
device_map='auto'
)
system_prompt = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information."""
def get_prompt(message: str, chat_history: list[tuple[str, str]]) -> str:
texts = [f'[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
for user_input, response in chat_history:
texts.append(f'{user_input.strip()} [/INST] {response.strip()} </s><s> [INST] ')
texts.append(f'{message.strip()} [/INST]')
return ''.join(texts)
print ("="*100)
print ("-"*80)
print ("Have a try!")
s = ''
chat_history = []
while True:
s = input("User: ")
if s != '':
prompt = get_prompt(s, chat_history)
print ('Answer:')
tokens = tokenizer(prompt, return_tensors='pt').input_ids
#generate_ids = model.generate(tokens.cuda(), max_new_tokens=4096, streamer=streamer)
generate_ids = model.generate(input_ids=tokens.cuda(), max_new_tokens=4096, streamer=streamer)
output = tokenizer.decode(generate_ids[0, len(tokens[0]):-1]).strip()
chat_history.append([s, output])
print ('-'*80)