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Model Details

Model Developers: Sogang University SGEconFinlab(<https://sc.sogang.ac.kr/aifinlab/)

Model Description

This model is a language model specialized in economics and finance. This was learned with various economic/finance-related data. The data sources are listed below, and we are not releasing the data that we trained on because it was used for research/policy purposes. If you wish to use the original data, please contact the original author directly for permission to use it.

Loading the Model

peft_model_id = "SGEcon/EconFinKoSOLAR-10.7B_SFT"
config = PeftConfig.from_pretrained(peft_model_id)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, quantization_config=bnb_config, device_map={"":0})
model = PeftModel.from_pretrained(model, peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model.eval()

Conducting Conversation

import re

def gen(x):
    inputs = tokenizer(f"### 질문: {x}\n\n### λ‹΅λ³€:", return_tensors='pt', return_token_type_ids=False)

    # Move data to GPU (if available)
    inputs = {k: v.to(device="cuda" if torch.cuda.is_available() else "cpu") for k, v in inputs.items()}

    gened = model.generate(
        **inputs,
        max_new_tokens=256,  # Maximum number of new tokens to create
        early_stopping=True,
        num_return_sequences=1,  # Generate only one answer
        do_sample=True,  # Enable sampling to generate a variety of answers
        eos_token_id=tokenizer.eos_token_id,  # Using EOS Token IDs 
        temperature=0.9,  # This option is adjustable.
        top_p=0.8,  # This option is adjustable.
        top_k=100  # This option is adjustable.
    )

    # Decode the generated sequence and convert it to output text 
    decoded = tokenizer.decode(gened[0], skip_special_tokens=True).strip()

    # Extract only text after a string "### λ‹΅λ³€:" 
    answer_start_idx = decoded.find("### λ‹΅λ³€:") + len("### λ‹΅λ³€:")
    complete_answer = decoded[answer_start_idx:].strip()

    # Find the first punctuation mark (. ? !) and extract only up to it
    match = re.search(r"[\.\?\!][^\.\?\!]*$", complete_answer)
    if match:
        complete_answer = complete_answer[:match.end()].strip()

    return complete_answer

Training Details

  • We train our model with PEFT. PEFT is a technique that does not tune all parameters of a model during fine-tuning, but only a small subset of parameters. By tuning only a few parameters while leaving others fixed, the model is less likely to suffer from catastrophic forgetting, where the model forgets previously learned tasks when it learns new ones. This significantly reduces computation and storage costs.

  • We use QLora to train the base model. Quantized Low Rank Adapters (QLoRA) is an efficient technique that uses 4-bit quantized pre-trained language models to fine-tune 65 billion parameter models on a 48 GB GPU while significantly reducing memory usage. The method uses NormalFloat 4-bit (NF4), a new data type that is theoretically optimal for normally distributed weights; Double Quantization, which further quantizes quantization constants to reduce average memory usage; and Paged Optimizers, which manage memory spikes during mini-batch processing, to increase memory efficiency without sacrificing performance.

  • Also, we performed instruction tuning using the data that we collected and the kyujinpy/KOR-OpenOrca-Platypus-v3 dataset on the hugging face. Instruction tuning is learning in a supervised learning format that uses instructions and input data together as input and output data as a pair. In other words, instruction tuning involves fine-tuning a pre-trained model for a specific task or set of tasks, where the model is taught to follow specific instructions or guidelines. Instruction tuning is a type of Supervised Fine-tuning (SFT) that aims to improve the generality and adaptability of a model by introducing an additional dimension that enables the model to understand and follow specific instructions.

Training Data

  1. ν•œκ΅­μ€ν–‰: κ²½μ œκΈˆμœ΅μš©μ–΄ 700μ„ (https://www.bok.or.kr/portal/bbs/B0000249/view.do?nttId=235017&menuNo=200765)
  2. κΈˆμœ΅κ°λ…μ›: κΈˆμœ΅μ†ŒλΉ„μž 정보 포털 파인 κΈˆμœ΅μš©μ–΄μ‚¬μ „(https://fine.fss.or.kr/fine/fnctip/fncDicary/list.do?menuNo=900021)
  3. KDI κ²½μ œμ •λ³΄μ„Όν„°: μ‹œμ‚¬ μš©μ–΄μ‚¬μ „(https://eiec.kdi.re.kr/material/wordDic.do)
  4. ν•œκ΅­κ²½μ œμ‹ λ¬Έ/ν•œκ²½λ‹·μ»΄: ν•œκ²½κ²½μ œμš©μ–΄μ‚¬μ „(https://terms.naver.com/list.naver?cid=42107&categoryId=42107), 였늘의 TESAT(https://www.tesat.or.kr/bbs.frm.list/tesat_study?s_cateno=1), 였늘의 μ£Όλ‹ˆμ–΄ TESAT(https://www.tesat.or.kr/bbs.frm.list/tesat_study?s_cateno=5), μƒκΈ€μƒκΈ€ν•œκ²½(https://sgsg.hankyung.com/tesat/study)
  5. μ€‘μ†Œλ²€μ²˜κΈ°μ—…λΆ€/λŒ€ν•œλ―Όκ΅­μ •λΆ€: μ€‘μ†Œλ²€μ²˜κΈ°μ—…λΆ€ μ „λ¬Έμš©μ–΄(https://terms.naver.com/list.naver?cid=42103&categoryId=42103)
  6. κ³ μ„±μ‚Ό/λ²•λ¬ΈμΆœνŒμ‚¬: νšŒκ³„Β·μ„Έλ¬΄ μš©μ–΄μ‚¬μ „(https://terms.naver.com/list.naver?cid=51737&categoryId=51737)
  7. 맨큐의 κ²½μ œν•™ 8판 Word Index
  8. kyujinpy/KOR-OpenOrca-Platypus-v3(https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3)

At the request of the original author, it is not to be used for commercial purposes. Therefore, it is licensed under the license CC-BY-NC-4.0. The copyright of the data used belongs to the original author, so please contact the original author when using it.

Training Hyperparameters

Hyperparameter SGEcon/KoSOLAR-10.7B-v0.2_fin_v4
Lora Method Lora
load in 4 bit True
learning rate 1e-5
lr scheduler linear
lora alpa 16
lora rank 16
lora dropout 0.05
optim paged_adamw_32bit
target_modules q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, lm_head

License

The language identification model is distributed under the Creative Commons Attribution-NonCommercial 4.0 International Public License.

Example

We only removed duplicate sentences.

μ€‘μ•™μ€ν–‰μ˜ 역할에 λŒ€ν•΄μ„œ μ„€λͺ…ν•΄μ€„λž˜?

쀑앙은행은 κ΅­κ°€κ°€ λ°œν–‰ν•˜λŠ” ν™”νμ˜ μœ ν†΅κ³Ό 경제의 μ•ˆμ •μ  μš΄μ˜μ„ μœ„ν•΄ μ„€λ¦½λœ κ΅­κ°€κΈ°κ΄€μž…λ‹ˆλ‹€. 쀑앙은행은 β‘  경제의 μ•ˆμ •μ  μš΄μ˜μ„ μœ„ν•΄ κΈˆμœ΅κΈ°κ΄€μ„ κ°λ…ν•˜κ³ , ν™”νμ˜ λ°œν–‰κ³Ό 톡화정책을 μ‹€μ‹œν•˜λŠ” κΈ°λŠ₯을 ν•©λ‹ˆλ‹€. β‘‘ ν†΅ν™”μ •μ±…μ˜ μ‹€ν–‰μ—λŠ” 쀑앙은행이 μœ λ™μ„±(Liquidity)을 κ³΅κΈ‰ν•˜κ³ , μ΄μžμœ¨μ„ μ‘°μ •ν•˜μ—¬ μˆ˜μš”μ™€ 곡급을 μ‘°μ ˆν•˜λŠ” 일, μ˜ˆκΈˆμ€€λΉ„κΈˆμ„ μœ μ§€ν•˜λŠ” 일, ν™˜μœ¨μ‘°μ ˆ 등이 ν¬ν•¨λ©λ‹ˆλ‹€. β‘’ 쀑앙은행은 κΈˆμœ΅κΈ°κ΄€μ˜ λŒ€μΆœν•œλ„ μ΄ˆκ³ΌλŒ€μΆœ, μ™Έν™˜κ±°λž˜, μ§€νμ˜ λ°œν–‰κ³Ό κ΅ν™˜μ„ κ΄€λ¦¬ν•˜λŠ” 업무도 λ‹΄λ‹Ήν•©λ‹ˆλ‹€. β‘£ ν†΅ν™”μ •μ±…μ˜ μ‹€μ‹œλŠ” 화폐와 μ‹ μš©μ˜ λ°œν–‰ 및 곡급을 ν†΅μ œλ₯Ό 톡해 λ¬Όκ°€μ•ˆμ •κ³Ό κ³ μš©μ„ μ¦λŒ€ν•˜κ³  경제의 κ· ν˜•μ„±μž₯을 도λͺ¨ν•˜λŠ” 것을 λͺ©ν‘œλ‘œ ν•˜κ³  μžˆμŠ΅λ‹ˆλ‹€.

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