---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---


# Model Details
Model Developers: Sogang University SGEconFinlab

### 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 we trained on because it was used for research/policy purposes. 
If you wish to use the original data rather than our training data, please contact the original author directly for permission to use it.

- **Developed by:** [Sogang University SGEconFinlab]
- **Language(s) (NLP):** [Ko/En]
- **License:** [apache-2.0]
- **Base Model:** [yanolja/KoSOLAR-10.7B-v0.2]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]


## How to Get Started with the Model


peft_model_id = "SGEcon/KoSOLAR-10.7B-v0.2_fin_v4"
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()


import re
def gen(x):
    inputs = tokenizer(f"### 질문: {x}\n\n### 답변:", return_tensors='pt', return_token_type_ids=False)
    
    # 데이터를 GPU로 이동(사용 가능한 경우)
    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,
        early_stopping=True,
        num_return_sequences=4,  # 4개의 답변을 생성하도록 설정(답변 개수 설정 가능)
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id,  # EOS 토큰 ID 사용
        temperature=0.9,
        top_p=0.8,
        top_k=50
    )
    
    complete_answers = []
    for gen_seq in gened:
        decoded = tokenizer.decode(gen_seq, skip_special_tokens=True).strip()

        # "### 답변:" 문자열 이후의 텍스트만 추출
        first_answer_start_idx = decoded.find("### 답변:") + len("### 답변:")
        temp_answer = decoded[first_answer_start_idx:].strip()

        # 두 번째 "### 답변:" 문자열 이전까지의 텍스트만 추출
        second_answer_start_idx = temp_answer.find("### 답변:")
        if second_answer_start_idx != -1:
            complete_answer = temp_answer[:second_answer_start_idx].strip()
        else:
            complete_answer = temp_answer  # 두 번째 "### 답변:"이 없는 경우 전체 답변 반환
        
        complete_answers.append(complete_answer)
    
    return complete_answers

    
## Training Details

### Training Data

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[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

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[More Information Needed]

### Results

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#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->