CC-KuLLM3-LoRA

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CC-KuLLM3-LoRA

This model is a fine-tuned version of nlpai-lab/KULLM3 on the AI-Hub/민원(콜센터) 질의-응답 데이터 and AI-Hub/용도별 목적대화 데이터 dataset. It achieves the following results on the evaluation set:

• Loss: 0.6711751222610474

This model is fine-tuned for "multi-turn conversations" in CC(Contact Center) domain.

Python code

import torch
from enum import Enum
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from peft import PeftModel
device = 'cuda' torch.cuda.is_available() else 'cpu'
base_model = AutoModelForCausalLM.from_pretrained(
    'nlpai-lab/KULLM3',
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map=device
)
tokenizer = AutoTokenizer.from_pretrained(
    'song9/CC-KuLLM3-LoRA'
)
base_model.resize_token_embeddings(len(tokenizer))
peft_model = PeftModel.from_pretrained(
    base_model,
    'song9/CC-KuLLM3-LoRA'
)
# inference
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
conversation = [
    {'role':'user','content':"지난주에 주문한 제품 반품하고 싶은데 어떻게 하죠?"}, # 이전 문맥
    {'role':'assistant','content':'반품 사유를 말씀해주시겠습니까?'}, # 이전 문맥
    {'role':'user','content':'아 네 제품이 배송 중에 파손된 것 같아요.'}
]
response_template = '[/INST]'
instruction_template = '[INST]'
collator = DataCollatorForCompletionOnlyLM(
    instruction_template=instruction_template,
    response_template=response_template,
    tokenizer=tokenizer
)
inputs = collator.tokenizer(
    tokenizer.apply_chat_template(
      conversation,
      tokenize=False,
      add_generation_prompt=True,
      return_tensors='pt'
    ),
    padding=True,
    truncation=True,
    return_tensors='pt'
).to(device)
output = peft_model.generate(inputs['input_ids'], streamer=streamer, max_new_tokens=1024, use_cache=True)
# print(tokenizer.decode(output[0]))
# 네 그러시군요. 고객님 불편을 드려 죄송합니다.

License

Apache-2.0 (following original repo)

Training and evaluation data

Dataset is preprocessed with two codes.

• AI-Hub/민원 질의-응답 데이터 preprocessing code : ai_hub_CC_QA_data_preprocessing_multithread_for_hf.py
• AI-Hub/용도별 목적대화 데이터 preprocessing code : ai_hub_multi_subject_conversations_data_preprocessing_multithread_for_hf.py

Set filepath and huggingface access token to yours and run the code. They refer to medium : fine-tuning-chat-based-llm.

Training procedure

Training Code is here : code
Referred this example : peft/examples

Training hyperparameters

The following hyperparameters were used during training:

• logging_steps: 800,
• save_steps: 8000,
• max_steps: 82422,
• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 2

Training results

Framework versions

• PEFT 0.13.2
• Transformers 4.45.1
• Pytorch 2.3.1+cu121
• Datasets 2.21.0
• Tokenizers 0.20.0