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traditional_chinese_qlora_llama2_merged

PEFT
Safetensors
Chinese
llama
llama2
qLoRa
traditional_chinese
alpaca
text-generation-inference
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metadata
library_name: peft
license: apache-2.0
tags:
  - llama2
  - qLoRa
  - traditional_chinese
  - alpaca
pipeline_tag: text-generation

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

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

# Use the same tokenizer from the source model
model_id="weiren119/traditional_chinese_qlora_llama2_merged"
tokenizer = AutoTokenizer.from_pretrained(original_model_path, use_fast=False)

# Load fine-tuned model, you can replace this with your own model
model = AutoPeftModelForCausalLM.from_pretrained(
        model_id,
        load_in_4bit=model_id.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)